{"url":"/dataset/coqa","name":"CoQA","full_name":"Conversational Question Answering Challenge","description_markdown":"**CoQA** is a large-scale dataset for building Conversational Question Answering systems. The goal of the CoQA challenge is to measure the ability of machines to understand a text passage and answer a series of interconnected questions that appear in a conversation.\r\n\r\nCoQA contains 127,000+ questions with answers collected from 8000+ conversations. Each conversation is collected by pairing two crowdworkers to chat about a passage in the form of questions and answers. The unique features of CoQA include 1) the questions are conversational; 2) the answers can be free-form text; 3) each answer also comes with an evidence subsequence highlighted in the passage; and 4) the passages are collected from seven diverse domains. CoQA has a lot of challenging phenomena not present in existing reading comprehension datasets, e.g., coreference and pragmatic reasoning.\r\n\r\nSource: [https://stanfordnlp.github.io/coqa/](https://stanfordnlp.github.io/coqa/)\r\nImage Source: [https://stanfordnlp.github.io/coqa/](https://stanfordnlp.github.io/coqa/)","description_withheld":null,"homepage":"https://stanfordnlp.github.io/coqa/","introduced_date":"2018-01-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/coqa-a-conversational-question-answering","title":"CoQA: A Conversational Question Answering Challenge","first_author":"Siva Reddy","url":null},"license":{"name":"Custom (multiple)","url":"https://stanfordnlp.github.io/coqa/#:~:text=License"},"modalities":[{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[{"name":"Question Answering","url":"/task/question-answering","datasets_with_task":"/datasets/task/question-answering"},{"name":"Reading Comprehension","url":"/task/reading-comprehension","datasets_with_task":"/datasets/task/reading-comprehension"},{"name":"Generative Question Answering","url":"/task/generative-question-answering","datasets_with_task":"/datasets/task/generative-question-answering"},{"name":"Conversational Question Answering","url":"/task/conversational-question-answering","datasets_with_task":"/datasets/task/conversational-question-answering"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["CoQA"],"data_loaders":[{"repo":"https://github.com/huggingface/datasets","url":"https://huggingface.co/datasets/coqa","frameworks":["tf","pytorch","jax"]},{"repo":"https://github.com/huggingface/datasets","url":"https://huggingface.co/datasets/Ruohao/pcmr","frameworks":["tf","pytorch","jax"]},{"repo":"https://github.com/huggingface/datasets","url":"https://huggingface.co/datasets/stanfordnlp/coqa","frameworks":["tf","pytorch","jax"]},{"repo":"https://github.com/facebookresearch/ParlAI","url":"https://parl.ai/docs/tasks.html#conversational-question-answering-challenge","frameworks":["pytorch"]},{"repo":"https://github.com/activeloopai/Hub","url":"https://docs.activeloop.ai/datasets/coqa-dataset","frameworks":["tf","pytorch"]},{"repo":"https://github.com/tensorflow/datasets","url":"https://www.tensorflow.org/datasets/catalog/coqa","frameworks":["tf","jax"]}],"num_papers_in_archive":281,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/question-answering-on-coqa","task":"Question Answering","dataset_variant":"CoQA","rows":9,"metrics":["In-domain","Out-of-domain","Overall"],"first_row_in_archive_order":{"model":"BERT Large Augmented (single 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are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/generative-question-answering-on-coqa","task":"Generative Question Answering","dataset_variant":"CoQA","rows":3,"metrics":["F1-Score"],"first_row_in_archive_order":{"model":"ERNIE-GEN","paper":"/paper/ernie-gen-an-enhanced-multi-flow-pre-training","metrics":{"F1-Score":"84.5"},"code_links":[{"title":"PaddlePaddle/PaddleNLP","url":"https://github.com/PaddlePaddle/PaddleNLP/blob/develop/paddlenlp/transformers/ernie_gen/modeling.py"},{"title":"Sharpiless/Versailles-text-generation-with-paddlepaddle","url":"https://github.com/Sharpiless/Versailles-text-generation-with-paddlepaddle"},{"title":"https-github-com-GiangHoang9912/ernie-gen","url":"https://github.com/https-github-com-GiangHoang9912/ernie-gen"},{"title":"MindSpore-scientific/code-12","url":"https://github.com/MindSpore-scientific/code-12/tree/main/FDDE/train-fine"},{"title":"MindCode-4/code-10","url":"https://github.com/MindCode-4/code-10/tree/main/FDDE/train-fine"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/language-models-are-few-shot-learners","title":"Language Models are Few-Shot Learners","date":"2020-05-28","rows_on_this_dataset":1,"code_links":67,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":65,"samples_ran":15,"samples_unverified":50,"pointer_only_for_licence":4,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/ernie-gen-an-enhanced-multi-flow-pre-training","title":"ERNIE-GEN: An Enhanced Multi-Flow Pre-training and Fine-tuning Framework for Natural Language Generation","date":"2020-01-26","rows_on_this_dataset":1,"code_links":5,"syntology":null},{"paper":"/paper/unified-language-model-pre-training-for","title":"Unified Language Model Pre-training for Natural Language Understanding and Generation","date":"2019-05-08","rows_on_this_dataset":1,"code_links":9,"syntology":null},{"paper":"/paper/sdnet-contextualized-attention-based-deep","title":"SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering","date":"2018-12-10","rows_on_this_dataset":2,"code_links":6,"syntology":null},{"paper":"/paper/bert-pre-training-of-deep-bidirectional","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","date":"2018-10-11","rows_on_this_dataset":2,"code_links":534,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":659,"samples_ran":204,"samples_unverified":455,"pointer_only_for_licence":149,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/flowqa-grasping-flow-in-history-for","title":"FlowQA: Grasping Flow in History for Conversational Machine Comprehension","date":"2018-10-06","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/a-qualitative-comparison-of-coqa-squad-20-and","title":"A Qualitative Comparison of CoQA, SQuAD 2.0 and QuAC","date":"2018-09-27","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/coqa-a-conversational-question-answering","title":"CoQA: A Conversational Question Answering Challenge","date":"2018-08-21","rows_on_this_dataset":3,"code_links":4,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":2,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":3,"samples_harvested":726,"samples_ran":221,"samples_unverified":505,"pointer_only_for_licence":153,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}