{"url":"/dataset/qaconv","name":"QAConv","full_name":null,"description_markdown":"**QAConv** is a new question answering (QA) dataset that uses conversations as a knowledge source. We focus on informative conversations including business emails, panel discussions, and work channels. Unlike opendomain and task-oriented dialogues, these conversations are usually long, complex, asynchronous, and involve strong domain knowledge. In total, we collect 34,204 QA pairs, including span-based, free-form, and unanswerable questions, from 10,259 selected conversations with both human-written and machine-generated questions. We segment long conversations into chunks, and use a question generator and dialogue summarizer as auxiliary tools to collect multi-hop questions. The dataset has two testing scenarios, chunk mode and full mode, depending on whether the grounded chunk is provided or retrieved from a large conversational pool.\r\n\r\nSource: [QAConv: Question Answering on Informative Conversations](https://arxiv.org/pdf/2105.06912v1.pdf)\r\n\r\nImage source: [QAConv: Question Answering on Informative Conversations](https://arxiv.org/pdf/2105.06912v1.pdf)","description_withheld":null,"homepage":"https://github.com/salesforce/QAConv","introduced_date":"2021-05-14","introduced_date_note":null,"introduced_by":{"paper":"/paper/qaconv-question-answering-on-informative","title":"QAConv: Question Answering on Informative Conversations","first_author":"Chien-Sheng Wu","url":null},"license":{"name":"Custom (research-only, non-commercial)","url":"https://github.com/salesforce/QAConv#ethics"},"modalities":[{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[{"name":"Question Answering","url":"/task/question-answering","datasets_with_task":"/datasets/task/question-answering"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["QAConv"],"data_loaders":[],"num_papers_in_archive":5,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"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."}