input
stringlengths 1.76k
1.91k
| output
sequencelengths 1
1
| id
stringlengths 40
40
|
|---|---|---|
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: James asked Robert for a favor but he was refused. Question: Who was refused?
Output:
| [
"Robert"
] | task492-50f31b19bca445749d734573717a0fef |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: Everyone really loved the oatmeal cookies; only a few people liked the snickerdoodle cookies. Next time, we should make more of them. Question: Which cookie should we make more of, next time?
Output:
| [
"snickerdoodle"
] | task492-009303afec5e4dbfbcd5c33ac517a688 |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: Grace was happy to trade me her sweater for my jacket. She thinks it looks dowdy on her. Question: What looks dowdy on Grace?
Output:
| [
"jacket"
] | task492-7686a7f9a176487c8e3f4ba27baf62e9 |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: Joe saw his brother skiing on TV last night but the fool didn't have a coat on Question: Who is the fool?
Output:
| [
"Joe"
] | task492-a59763b67a584a589657c93794b5c9ed |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: During a game of tag, Ethan chased Luke because he was "it". Question: Who was "it"?
Output:
| [
"Luke"
] | task492-e3b5aff0690f41468cd5c22db4ee1abd |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: Emma's mother had died long ago, and her education] had been [taken by an excellent woman as governess. Question: Whose education] had been [taken?
Output:
| [
"mother's"
] | task492-c9f44dbc033e4f548a903410f55f248a |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: Bill thinks that calling attention to himself was rude to Bert. Question: Who called attention to himself?
Output:
| [
"Bert"
] | task492-860754d9a2f1449eaf741e42b6bcc34e |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: I tried to paint a picture of an orchard, with lemons in the lemon trees, but they came out looking more like telephone poles. Question: What looked like telephone poles?
Output:
| [
"lemons"
] | task492-4a4ea8995fa14149b4e3c2ba7d801016 |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: I couldn't find a spoon, so I tried using a pen to stir my coffee. But that turned out to be a bad idea, because it got full of ink. Question: What got full of ink?
Output:
| [
"pen"
] | task492-80a0b2838d184e329ff222f9be9e4ce0 |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: When Tommy dropped his ice cream, Timmy giggled, so father gave him a stern look. Question: Who got the look from father?
Output:
| [
"Tommy"
] | task492-ad73c3f4e535493a8dddbc4774bc6716 |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: The woman held the girl against her chest. Question: Whose chest?
Output:
| [
"girl's"
] | task492-997500039f454b5b9c0750da20685ce3 |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: Tom gave Ralph a lift to school so he wouldn't have to drive alone. Question: Who wouldn't have to drive alone?
Output:
| [
"Ralph"
] | task492-35bbba8881b94ac4812397f6413723e8 |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: Jane knocked on Susan's door but she did not answer. Question: Who did not answer?
Output:
| [
"Jane"
] | task492-9e385a9ad3bb47c0b499ccc77cb277ba |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: I stuck a pin through a carrot. When I pulled the pin out, it had a hole. Question: What had a hole?
Output:
| [
"pin"
] | task492-9c13ed519b0846e18fa15260ce9f1d75 |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: As Andrea in the crop duster passed over Susan, she could see the landing strip. Question: Who could see the landing strip?
Output:
| [
"Susan"
] | task492-c92a83a02f3548d6a7bd80ddd4db3fd2 |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: Bill thinks that calling attention to himself was rude of Bert. Question: Who called attention to himself?
Output:
| [
"Bill"
] | task492-0ec065f8e7294eaa96e7017251f60114 |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: Sam Goodman's biography of the Spartan general Xenophanes conveys a vivid sense of the difficulties he faced in his research. Question: Who faced difficulties?
Output:
| [
"Xenophanes"
] | task492-cb0def07574d47a7947eecf118abef11 |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: Tom said "Check" to Ralph as he moved his bishop. Question: Who owned the bishop that Tom moved?
Output:
| [
"Ralph"
] | task492-2f5ced9b80ff482aa5d92edc76fb3385 |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: The police arrested all of the criminals. They were trying to stop the drug trade in the neighborhood. Question: Who was trying to stop the drug trade?
Output:
| [
"criminals"
] | task492-972b9f7100fa4fb8900a9cf064168fda |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: As Ollie carried Tommy up the long winding steps, his legs ached. Question: Whose legs ached?
Output:
| [
"Tommy"
] | task492-63072a6310504b4898245721a3a472c8 |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: In July, Kamtchatka declared war on Yakutsk. Since Yakutsk's army was much better equipped and ten times larger, they were defeated within weeks. Question: Who was defeated
Output:
| [
"Yakutsk"
] | task492-beffbd4c22f5438b9676f97d70d3f89b |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: Emma did not pass the ball to Janie although she saw that she was open. Question: Who saw that the other player was open?
Output:
| [
"Janie"
] | task492-ce56d213fd624947a69c16ae022c78db |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: There are too many deer in the park, so the park service brought in a small pack of wolves. The population should increase over the next few years. Question: Which population will increase?
Output:
| [
"deer"
] | task492-e94b2d2bae12429ba6114e6743a8ff6f |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: Kirilov ceded the presidency to Shatov because he was less popular. Question: Who was less popular?
Output:
| [
"Shatov"
] | task492-d218486ce7f847a39aeb63d341f056d0 |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: Sam Goodman's biography of the Spartan general Xenophanes conveys a vivid sense of the difficulties he faced in his childhood. Question: Who faced difficulties?
Output:
| [
"Sam"
] | task492-43955dffe51a433babd5aa026c500c5c |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: Esther figures that she will save shipping costs if she builds her factory in Springfield instead of Franklin, because most of her customers live there. Question: In which town do most of Esther's customers live?
Output:
| [
"Franklin"
] | task492-fdc0343ee7d64b4187bfe20f97d45195 |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: Alice looked for her friend Jade in the crowd. Since she always wears a red turban, Alice spotted her quickly. Question: Who always wears a red turban
Output:
| [
"Alice"
] | task492-5045c6f1b2774257b3c22dec14c9c03c |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: Joe saw his brother skiing on TV last night but the fool didn't recognize him Question: Who is the fool?
Output:
| [
"brother"
] | task492-8962a500e1d04e328315e180fcff6099 |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: Joe paid the detective after he received the final report on the case. Question: Who received the final report?
Output:
| [
" detective"
] | task492-88c0af5454a24ab4adb026f60bae10fa |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: I stuck a pin through a carrot. When I pulled the pin out, it left a hole. Question: What left a hole?
Output:
| [
"carrot"
] | task492-4779135e08804c46adb1a4dbf454c5a0 |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: Dan had to stop Bill from toying with the injured bird. He is very cruel. Question: Who is cruel?
Output:
| [
"Dan"
] | task492-33beb1f4f0764809a2a65ae0f55764c9 |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: Tom gave Ralph a lift to school so he wouldn't have to walk. Question: Who wouldn't have to walk?
Output:
| [
"Tom"
] | task492-238db9de0c2147369660b6b1cbe6765d |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: The scientists are studying three species of fish that have recently been found living in the Indian Ocean. They appeared two years ago. Question: Who or what appeared two years ago?
Output:
| [
"scientists"
] | task492-6502ee07c9674f5a9770216d6b2c7364 |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: Billy cried because Toby wouldn't share his toy. Question: Who owned the toy?
Output:
| [
"Billy"
] | task492-595155132d3542959ec26e0fa852b0d2 |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: The table won't fit through the doorway because it is too narrow. Question: What is too narrow?
Output:
| [
"table"
] | task492-dd3d4e1acba843819995bc24a55465ca |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: Fred is the only man alive who still remembers my father as an infant. When Fred first saw my father, he was twelve months old. Question: Who was twelve months old?
Output:
| [
"Fred"
] | task492-8ac6d9964fbe40b0b85b38a720f4e46e |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: The journalists interviewed the stars of the new movie. They were very persistent, so the interview lasted for a long time. Question: Who was persistent?
Output:
| [
"stars"
] | task492-9e2435df75b242bfaea07419eec28856 |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: Stretching her back, the woman smiled at the girl. Question: Whose back was the woman stretching?
Output:
| [
"girl's"
] | task492-6b1f66a05b944c1d90487472787af44c |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: The man lifted the boy onto his bunk bed. Question: Whose bunk bed?
Output:
| [
"man's"
] | task492-1c60fb11778e4b3a84fb35ccdf36b4a8 |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: Bill passed the gameboy to John because his turn was next. Question: Whose turn was next?
Output:
| [
"Bill's"
] | task492-4556bba0e6924e10aac31641bdf01b9a |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: James asked Robert for a favor but he refused. Question: Who refused?
Output:
| [
"James"
] | task492-c1345b7f1bb64d3cbb67ada82ee7649c |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: Carol believed that Rebecca suspected that she had stolen the watch. Question: Who is suspected of stealing the watch?
Output:
| [
"Rebecca"
] | task492-2b66dccdc7e348f0949ae880d72614df |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: Beth didn't get angry with Sally, who had cut her off, because she stopped and apologized. Question: Who apologized?
Output:
| [
"Beth"
] | task492-10211615c873476a840e16a4deff8704 |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: Patting her back, the woman smiled at the girl. Question: Whose back was the woman patting?
Output:
| [
"woman's"
] | task492-b40f18de948540cb9a7adc2d916c0582 |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: There are too many deer in the park, so the park service brought in a small pack of wolves. The population should decrease over the next few years. Question: Which population will decrease?
Output:
| [
"wolves"
] | task492-9e563fb14e384312968463024aba0edf |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: As Ollie carried Tommy up the long winding steps, his legs dangled. Question: Whose legs dangled?
Output:
| [
"Ollie"
] | task492-a5a2e717860e46b79b393d3d997f8258 |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: Dan took the rear seat while Bill claimed the front because his "Dibs!" was quicker. Question: Whose "Dibs" was quicker?
Output:
| [
"Dan's"
] | task492-e5265b95342a44b48b030e92d03b8f0b |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: The doctor informed Kate that she had retired and presented several options for future treatment. Question: Who had retired?
Output:
| [
"Kate"
] | task492-baa6286afcf445ebb38d9fd29528d9ad |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: We had hoped to place copies on all the chairs in the auditorium, but there were simply not enough of them. Question: There are too many of what?
Output:
| [
"copies"
] | task492-32f87e0f51954aba956010f919f20056 |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: Alice looked for her friend Jade in the crowd. Since she always has good luck, Alice spotted her quickly. Question: Who always has good luck
Output:
| [
"Jade"
] | task492-383ea945aec04ffb835e52e0fbf2d54b |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: Lily spoke to Donna, breaking her silence. Question: Whose silence?
Output:
| [
"Donna's"
] | task492-3a05e33581f44c9f9be49aad3b0e9111 |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: The scientists are studying three species of fish that have recently been found living in the Indian Ocean. They began two years ago. Question: Who or what began two years ago?
Output:
| [
"fish"
] | task492-a50492e7fc424474bc2fd6cd8f44c2d4 |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: We had hoped to place copies on all the chairs in the auditorium, but there were simply too many of them. Question: There are not enough of what?
Output:
| [
"chairs"
] | task492-33d4ec59c9cc496181dfb3d6d1c13908 |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: When the sponsors of the bill got to the town hall, they were surprised to find that the room was full of opponents. They were very much in the majority. Question: Who were in the majority?
Output:
| [
"sponsors"
] | task492-b45ac0040d10440ba8ceb44098da4d93 |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: I put the cake away in the refrigerator. It has a lot of butter in it. Question: What has a lot of butter?
Output:
| [
"refrigerator"
] | task492-9dcbfbcbfd0d4ea39549d747f6198020 |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: The father carried the sleeping boy in his bassinet. Question: Whose bassinet?
Output:
| [
"father"
] | task492-99627eea2b73412da7deb04365540dd3 |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: The table won't fit through the doorway because it is too wide. Question: What is too wide?
Output:
| [
"doorway"
] | task492-c0801a2a1503462cb11a3e3e13a6e40d |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: John ordered Bill to leave, so an hour later he left. Question: Who left?
Output:
| [
"John"
] | task492-5c1165e96bb0437fa681addef8ee827e |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: The man lifted the boy onto his shoulders. Question: Whose shoulders?
Output:
| [
"boy's"
] | task492-db41a491ecf5452a87d48fb8cfae6b5c |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: Sam broke both his ankles and he's walking with crutches. But a month or so from now they should be better. Question: What should be better?
Output:
| [
"crutches"
] | task492-a50deb54f0564c87a2173807b0b8f5fe |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: The police arrested all of the criminals. They were trying to run the drug trade in the neighborhood. Question: Who was trying to run the drug trade?
Output:
| [
"police"
] | task492-3e45f16aa8af4175b9607c0d10532094 |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: Grace was happy to trade me her sweater for my jacket. She thinks it looks great on her. Question: What looks great on Grace?
Output:
| [
"sweater"
] | task492-1dff0a649dec4b5a9273faa0bc9e6d3e |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: Fred is the only man alive who still remembers my father as an infant. When Fred first saw my father, he was twelve years old. Question: Who was twelve years old?
Output:
| [
"father"
] | task492-3f31a2fc560b43a0993e9265bbd212d0 |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: Bill passed the gameboy to John because his turn was over. Question: Whose turn was over?
Output:
| [
"John's"
] | task492-e7fbb2e09e41414ab0ebd5a2a519bc46 |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: Esther figures that she will save shipping costs if she builds her factory in Springfield instead of Franklin, because none of her customers live there. Question: In which town do none of Esther's customers live?
Output:
| [
"Springfield"
] | task492-5e04a8ce9d6b487cb40abe8129a5b9f5 |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: In July, Kamtchatka declared war on Yakutsk. Since Yakutsk's army was much better equipped and ten times larger, they were victorious within weeks. Question: Who was victorious
Output:
| [
"Kamchatka"
] | task492-9092ec854ade4c4fb37824345a384427 |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: The father carried the sleeping boy in his arms. Question: Whose arms?
Output:
| [
"boy"
] | task492-fbc9ea0e138146ec8a45c4b9e4b2aabd |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: The journalists interviewed the stars of the new movie. They were very cooperative, so the interview lasted for a long time. Question: Who was cooperative?
Output:
| [
"journalists"
] | task492-3461f10e701240e8acab23a42c9bc5d1 |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: Beth didn't get angry with Sally, who had cut her off, because she stopped and counted to ten. Question: Who counted to ten?
Output:
| [
"Sally"
] | task492-d91fc346153e4e2ba9eb3bf48026c527 |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: Bill passed the half-empty plate to John because he was hungry. Question: Who was hungry?
Output:
| [
"Bill"
] | task492-9634a21e26594fe187b3ed26d070a72a |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: Carol believed that Rebecca regretted that she had stolen the watch. Question: Who is stole the watch?
Output:
| [
"Carol"
] | task492-8e09bd335086478a8759e65c0816fede |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: The archaeologists have concluded that neanderthals lived in Laputa 20,000 years ago. They hunted for evidence on the river banks. Question: Who hunted for evidence?
Output:
| [
"neanderthals"
] | task492-953d5cb9ad8c4054b72fca6e7d5fdc0b |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: I put the cake away in the refrigerator. It has a lot of leftovers in it. Question: What has a lot of leftovers?
Output:
| [
"cake"
] | task492-e39bcc382da440d88bc8eee4d497fbd3 |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: Elizabeth moved her company from Sparta to Troy to save money on taxes; the taxes are much lower there. Question: Where are the taxes lower?
Output:
| [
"Sparta"
] | task492-1d4f67ae47774b09b6134265b4be6a58 |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: Billy cried because Toby wouldn't accept his toy. Question: Who owned the toy?
Output:
| [
"Toby"
] | task492-69847a723972403ba91e68b33a699a10 |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: Jane knocked on Susan's door but she did not get an answer. Question: Who did not get an answer?
Output:
| [
"Susan"
] | task492-b2631017ff6f49a8b7678495fc001e82 |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: The archaeologists have concluded that neanderthals lived in Laputa 20,000 years ago. They hunted for deer on the river banks. Question: Who hunted for deer?
Output:
| [
"archaeologists"
] | task492-640fe216eb0a4f75bf814048a519592e |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: Kirilov ceded the presidency to Shatov because he was more popular. Question: Who was more popular?
Output:
| [
"Kirilov"
] | task492-872fbde2c2a6451ba64cc918a7b6600d |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: Elizabeth moved her company from Sparta to Troy to save money on taxes; the taxes are much higher there. Question: Where are the taxes higher?
Output:
| [
"Troy"
] | task492-5b575149baca4f4c8ab3e310b29d7064 |
Definition: In this task, based on the given sentence and the question, you are asked to generate an incorrect answer. The incorrect answer has to be a text span from the given sentence. Note that, the correct answer to the given question will require understanding of coreference resolution. Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. For example let's take a sentence 'I voted for Obama because he was most aligned with my values, she said.' Here in this example 'I', 'my', and 'she' belong to the same cluster and 'Obama' and 'he' belong to the same cluster. Now let's discuss another example , original sentence: 'I voted for Trump because he was most aligned with my values',John said. Now here is the same sentence with resolved coreferences: 'John voted for Trump because Trump was most aligned with John's values',John said.
Positive Example 1 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to guard them. Question: What do I have to guard?
Output: foxes
Positive Example 2 -
Input: Sentence: The foxes are getting in at night and attacking the chickens. I shall have to kill them. Question: What do I have to kill?
Output: chickens
Negative Example 1 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to answer it. Question: Who was reluctant to answer the question?
Output: witness
Negative Example 2 -
Input: Sentence: The lawyer asked the witness a question, but he was reluctant to repeat it. Question: Who was reluctant to repeat the question?
Output: layer
Now complete the following example -
Input: Sentence: Dan had to stop Bill from toying with the injured bird. He is very compassionate. Question: Who is compassionate?
Output:
| [
"Bill"
] | task492-81d044b9e9ff48e1b7d161819c73d071 |
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task492_mwsc_incorrect_answer_generation
Additional Information
Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks},
author={Yizhong Wang and Swaroop Mishra and Pegah Alipoormolabashi and Yeganeh Kordi and Amirreza Mirzaei and Anjana Arunkumar and Arjun Ashok and Arut Selvan Dhanasekaran and Atharva Naik and David Stap and Eshaan Pathak and Giannis Karamanolakis and Haizhi Gary Lai and Ishan Purohit and Ishani Mondal and Jacob Anderson and Kirby Kuznia and Krima Doshi and Maitreya Patel and Kuntal Kumar Pal and Mehrad Moradshahi and Mihir Parmar and Mirali Purohit and Neeraj Varshney and Phani Rohitha Kaza and Pulkit Verma and Ravsehaj Singh Puri and Rushang Karia and Shailaja Keyur Sampat and Savan Doshi and Siddhartha Mishra and Sujan Reddy and Sumanta Patro and Tanay Dixit and Xudong Shen and Chitta Baral and Yejin Choi and Noah A. Smith and Hannaneh Hajishirzi and Daniel Khashabi},
year={2022},
eprint={2204.07705},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2204.07705},
}
More details can also be found in the following paper:
@misc{brüelgabrielsson2024compressserveservingthousands,
title={Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead},
author={Rickard Brüel-Gabrielsson and Jiacheng Zhu and Onkar Bhardwaj and Leshem Choshen and Kristjan Greenewald and Mikhail Yurochkin and Justin Solomon},
year={2024},
eprint={2407.00066},
archivePrefix={arXiv},
primaryClass={cs.DC},
url={https://arxiv.org/abs/2407.00066},
}
Contact Information
For any comments or questions, please email Rickard Brüel Gabrielsson
- Downloads last month
- 48