Datasets:
Tasks:
Question Answering
Modalities:
Text
Formats:
parquet
Sub-tasks:
extractive-qa
Size:
10K - 100K
License:
| annotations_creators: | |
| - crowdsourced | |
| language: | |
| - en | |
| - ar | |
| - bn | |
| - fi | |
| - ja | |
| - ko | |
| - ru | |
| - te | |
| language_creators: | |
| - crowdsourced | |
| license: | |
| - mit | |
| multilinguality: | |
| - multilingual | |
| pretty_name: XORQA Reading Comprehension | |
| size_categories: | |
| - '10K<n<100K' | |
| source_datasets: | |
| - extended|wikipedia | |
| task_categories: | |
| - question-answering | |
| task_ids: | |
| - extractive-qa | |
| # Dataset Card for "tydi_xor_rc_yes_no_unanswerable" | |
| ## Dataset Description | |
| - **Homepage:** [https://github.com/google-research-datasets/tydiqa](https://github.com/google-research-datasets/tydiqa) | |
| - **Paper:** [Paper](https://aclanthology.org/2021.naacl-main.46) | |
| ### Dataset Summary | |
| [TyDi QA](https://huggingface.co/datasets/tydiqa) is a question answering dataset covering 11 typologically diverse languages. | |
| [XORQA](https://github.com/AkariAsai/XORQA) is an extension of the original TyDi QA dataset to also include unanswerable questions, where context documents are only in English but questions are in 7 languages. | |
| This dataset is a simplified version of the [Reading Comprehension data](https://nlp.cs.washington.edu/xorqa/XORQA_site/data/tydi_xor_rc_yes_no_unanswerable.zip) from XORQA. | |
| ## Dataset Structure | |
| The dataset contains a train and a validation set, with 15445 and 3646 examples, respectively. Access them with | |
| ```py | |
| from datasets import load_dataset | |
| dataset = load_dataset("coastalcph/tydi_xor_rc_yes_no_unanswerable") | |
| train_set = dataset["train"] | |
| validation_set = dataset["validation"] | |
| ``` | |
| ### Data Instances | |
| Description of the dataset columns: | |
| | Column name | type | Description | | |
| | ----------- | ----------- | ----------- | | |
| | lang | str | The language of the data instance | | |
| | question | str | The question to answer | | |
| | context | str | The context, a Wikipedia paragraph that might or might not contain the answer to the question | | |
| | is_impossible | bool | FALSE if the question can be answered given the context, TRUE otherwise | | |
| | answer_start | int | The character index in 'context' where the answer starts. If the question is unanswerable, this is -1 | | |
| | answer | str | The answer, a span of text from 'context'. If the question is unanswerable given the context, this can be 'yes' or 'no' | | |
| ## Useful stuff | |
| Check out the [datasets ducumentations](https://huggingface.co/docs/datasets/quickstart) to learn how to manipulate and use the dataset. Specifically, you might find the following functions useful: | |
| `dataset.filter`, for filtering out data (useful for keeping instances of specific languages, for example). | |
| `dataset.map`, for manipulating the dataset. | |
| `dataset.to_pandas`, to convert the dataset into a pandas.DataFrame format. | |
| ``` | |
| @inproceedings{xorqa, | |
| title = {{XOR} {QA}: Cross-lingual Open-Retrieval Question Answering}, | |
| author = {Akari Asai and Jungo Kasai and Jonathan H. Clark and Kenton Lee and Eunsol Choi and Hannaneh Hajishirzi}, | |
| booktitle={NAACL-HLT}, | |
| year = {2021} | |
| } | |
| ``` | |
| ``` | |
| @article{tydiqa, | |
| title = {TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages}, | |
| author = {Jonathan H. Clark and Eunsol Choi and Michael Collins and Dan Garrette and Tom Kwiatkowski and Vitaly Nikolaev and Jennimaria Palomaki} | |
| year = {2020}, | |
| journal = {Transactions of the Association for Computational Linguistics} | |
| } | |
| ``` | |