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| "description": "CommonsenseQA is a new multiple-choice question answering dataset that requires different types of commonsense knowledge\nto predict the correct answers . It contains 12,102 questions with one correct answer and four distractor answers.\nThe dataset is provided in two major training/validation/testing set splits: \"Random split\" which is the main evaluation\nsplit, and \"Question token split\", see paper for details.\n\n\n\nOur dataset is gathered by using a new representation language to annotate over the AQuA-RAT dataset. AQuA-RAT has provided the questions, options, rationale, and the correct options.", |
| "citation": "@inproceedings{talmor-etal-2019-commonsenseqa,\n title = \"{C}ommonsense{QA}: A Question Answering Challenge Targeting Commonsense Knowledge\",\n author = \"Talmor, Alon and\n Herzig, Jonathan and\n Lourie, Nicholas and\n Berant, Jonathan\",\n booktitle = \"Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)\",\n month = jun,\n year = \"2019\",\n address = \"Minneapolis, Minnesota\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/N19-1421\",\n doi = \"10.18653/v1/N19-1421\",\n pages = \"4149--4158\",\n archivePrefix = \"arXiv\",\n eprint = \"1811.00937\",\n primaryClass = \"cs\",\n}", |
| "homepage": "https://www.tau-nlp.org/commonsenseqa\n\nhttps://math-qa.github.io/math-QA/", |
| "license": "", |
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| "dataset_name": "qna_datasets" |
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