metadata

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
Paper: Paper

Dataset Summary

TyDi QA is a question answering dataset covering 11 typologically diverse languages. 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 from XORQA.

Dataset Structure

The dataset contains a train and a validation set, with 15445 and 3646 examples, respectively. Access them with

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 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}
}