{"url":"/dataset/clariq","name":"ClariQ","full_name":null,"description_markdown":"ClariQ is an extension of the Qulac dataset with additional new topics, questions, and answers in the training set. The test set is completely unseen and newly collected. Like Qulac, ClariQ consists of single-turn conversations (initial_request, followed by clarifying question and answer). In addition, it comes with synthetic multi-turn conversations (up to three turns). ClariQ features approximately 18K single-turn conversations, as well as 1.8 million multi-turn conversations. \r\n\r\nSource: [ConvAI3: Generating Clarifying Questions for Open-Domain Dialogue Systems (ClariQ)](/paper/convai3-generating-clarifying-questions-for)","description_withheld":null,"homepage":"https://github.com/aliannejadi/ClariQ","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/convai3-generating-clarifying-questions-for","title":"ConvAI3: Generating Clarifying Questions for Open-Domain Dialogue Systems (ClariQ)","first_author":"Mohammad Aliannejadi","url":null},"license":null,"modalities":[{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[{"name":"Information Retrieval","url":"/task/information-retrieval","datasets_with_task":"/datasets/task/information-retrieval"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["ClariQ"],"data_loaders":[{"repo":"https://github.com/aliannejadi/ClariQ","url":"https://github.com/aliannejadi/ClariQ","frameworks":[]}],"num_papers_in_archive":11,"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."}