{"url":"/dataset/convref","name":"ConvRef","full_name":null,"description_markdown":"**ConvRef** is a conversational QA benchmark with reformulations. \r\nIt consists of around 11k natural conversations with about 205k reformulations.\r\nConvRef builds upon the conversational KG-QA benchmark [ConvQuestions](/dataset/convquestions).\r\nQuestions come from five different domains: books, movies, music, TV series and soccer and answers are Wikidata entities.\r\n We used conversation sessions in ConvQuestions as input to our user study. Study participants interacted with a baseline QA system, that was trained using the available paraphrases in ConvQuestions as proxies for reformulations. Users were shown follow-up questions in a given conversation interactively, one after the other, along with the answer coming from the baseline QA system. For wrong answers, the user was prompted to reformulate the question up to four times if needed. In this way, users were able to pose reformulations based on previous wrong answers and the conversation history.","description_withheld":null,"homepage":"https://conquer.mpi-inf.mpg.de/","introduced_date":"2021-05-11","introduced_date_note":null,"introduced_by":{"paper":"/paper/reinforcement-learning-from-reformulations-in","title":"Reinforcement Learning from Reformulations in Conversational Question Answering over Knowledge Graphs","first_author":"Magdalena Kaiser","url":null},"license":{"name":"CC BY","url":"https://creativecommons.org/licenses/by/4.0/"},"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":["ConvRef"],"data_loaders":[],"num_papers_in_archive":4,"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."}