Datasets:
Tasks:
Question Answering
Modalities:
Text
Formats:
parquet
Sub-tasks:
extractive-qa
Size:
10K - 100K
License:
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"
Dataset Description
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. XOR-AttriQA contains annotated attribution data for a sample of XORQA. This dataset is a combined and simplified version of the Reading Comprehension data from XORQA and the in-English data from XOR-AttriQA.
The code to create the dataset is available on this Colab notebook.
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")
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 question |
| question | str | The question to answer |
| context | str | The context, a Wikipedia paragraph in English that might or might not contain the answer to the question |
| answertable | bool | True if the question can be answered given the context, False otherwise |
| answer_start | int | The character index in 'context' where the answer starts. If the question is unanswerable given the context, this is -1 |
| answer | str | The answer in English, a span of text from 'context'. If the question is unanswerable given the context, this can be 'yes' or 'no' |
| answer_inlang | str | The answer in the same language as the question, only available for some instances (otherwise, NaN) |
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.
Citations
@article{clark-etal-2020-tydi,
title = "{T}y{D}i {QA}: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages",
author = "Clark, Jonathan H. and
Choi, Eunsol and
Collins, Michael and
Garrette, Dan and
Kwiatkowski, Tom and
Nikolaev, Vitaly and
Palomaki, Jennimaria",
editor = "Johnson, Mark and
Roark, Brian and
Nenkova, Ani",
journal = "Transactions of the Association for Computational Linguistics",
volume = "8",
year = "2020",
address = "Cambridge, MA",
publisher = "MIT Press",
url = "https://aclanthology.org/2020.tacl-1.30",
doi = "10.1162/tacl_a_00317",
pages = "454--470",
abstract = "Confidently making progress on multilingual modeling requires challenging, trustworthy evaluations. We present TyDi QA{---}a question answering dataset covering 11 typologically diverse languages with 204K question-answer pairs. The languages of TyDi QA are diverse with regard to their typology{---}the set of linguistic features each language expresses{---}such that we expect models performing well on this set to generalize across a large number of the world{'}s languages. We present a quantitative analysis of the data quality and example-level qualitative linguistic analyses of observed language phenomena that would not be found in English-only corpora. To provide a realistic information-seeking task and avoid priming effects, questions are written by people who want to know the answer, but don{'}t know the answer yet, and the data is collected directly in each language without the use of translation.",
}
@inproceedings{asai-etal-2021-xor,
title = "{XOR} {QA}: Cross-lingual Open-Retrieval Question Answering",
author = "Asai, Akari and
Kasai, Jungo and
Clark, Jonathan and
Lee, Kenton and
Choi, Eunsol and
Hajishirzi, Hannaneh",
editor = "Toutanova, Kristina and
Rumshisky, Anna and
Zettlemoyer, Luke and
Hakkani-Tur, Dilek and
Beltagy, Iz and
Bethard, Steven and
Cotterell, Ryan and
Chakraborty, Tanmoy and
Zhou, Yichao",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.46",
doi = "10.18653/v1/2021.naacl-main.46",
pages = "547--564",
abstract = "Multilingual question answering tasks typically assume that answers exist in the same language as the question. Yet in practice, many languages face both information scarcity{---}where languages have few reference articles{---}and information asymmetry{---}where questions reference concepts from other cultures. This work extends open-retrieval question answering to a cross-lingual setting enabling questions from one language to be answered via answer content from another language. We construct a large-scale dataset built on 40K information-seeking questions across 7 diverse non-English languages that TyDi QA could not find same-language answers for. Based on this dataset, we introduce a task framework, called Cross-lingual Open-Retrieval Question Answering (XOR QA), that consists of three new tasks involving cross-lingual document retrieval from multilingual and English resources. We establish baselines with state-of-the-art machine translation systems and cross-lingual pretrained models. Experimental results suggest that XOR QA is a challenging task that will facilitate the development of novel techniques for multilingual question answering. Our data and code are available at \url{https://nlp.cs.washington.edu/xorqa/}.",
}
@inproceedings{muller-etal-2023-evaluating,
title = "Evaluating and Modeling Attribution for Cross-Lingual Question Answering",
author = "Muller, Benjamin and
Wieting, John and
Clark, Jonathan and
Kwiatkowski, Tom and
Ruder, Sebastian and
Soares, Livio and
Aharoni, Roee and
Herzig, Jonathan and
Wang, Xinyi",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.10",
doi = "10.18653/v1/2023.emnlp-main.10",
pages = "144--157",
abstract = "Trustworthy answer content is abundant in many high-resource languages and is instantly accessible through question answering systems {---} yet this content can be hard to access for those that do not speak these languages. The leap forward in cross-lingual modeling quality offered by generative language models offers much promise, yet their raw generations often fall short in factuality. To improve trustworthiness in these systems, a promising direction is to attribute the answer to a retrieved source, possibly in a content-rich language different from the query. Our work is the first to study attribution for cross-lingual question answering. First, we collect data in 5 languages to assess the attribution level of a state-of-the-art cross-lingual QA system. To our surprise, we find that a substantial portion of the answers is not attributable to any retrieved passages (up to 50{\%} of answers exactly matching a gold reference) despite the system being able to attend directly to the retrieved text. Second, to address this poor attribution level, we experiment with a wide range of attribution detection techniques. We find that Natural Language Inference models and PaLM 2 fine-tuned on a very small amount of attribution data can accurately detect attribution. With these models, we improve the attribution level of a cross-lingual QA system. Overall, we show that current academic generative cross-lingual QA systems have substantial shortcomings in attribution and we build tooling to mitigate these issues.",
}