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" | |
| ## Dataset Description | |
| - Homepage: https://ai.google.com/research/tydiqa | |
| - Paper: https://aclanthology.org/2020.tacl-1.30 | |
| ### 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. | |
| [XOR-AttriQA](https://github.com/google-research/google-research/tree/master/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](https://nlp.cs.washington.edu/xorqa/XORQA_site/data/tydi_xor_rc_yes_no_unanswerable.zip) and the [in-English data from XOR-AttriQA](https://storage.googleapis.com/gresearch/xor_attriqa/xor_attriqa.zip). | |
| The code to create the dataset is available on [this Colab notebook](https://colab.research.google.com/drive/14s0FEag5FDr-jqjaVLzlU_0Lv0nXHWNg?usp=sharing). | |
| ## 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") | |
| 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](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. | |
| ## 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.", | |
| } | |
| ``` | |