Dataset Card for "tydi_xor_rc"

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 15343 and 3011 examples, respectively. Access them with

from datasets import load_dataset
dataset = load_dataset("coastalcph/tydi_xor_rc")
df_train = dataset["train"].to_pandas()
df_val = dataset["validation"].to_pandas()

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.",
}