Datasets:
Tasks:
Question Answering
Modalities:
Text
Formats:
parquet
Sub-tasks:
extractive-qa
Size:
10K - 100K
License:
File size: 3,811 Bytes
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---
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}
}
```
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