{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/comqa-a-community-sourced-dataset-for-complex","title":"ComQA: A Community-sourced Dataset for Complex Factoid Question Answering with Paraphrase Clusters","arxiv_id":"1809.09528","date":"2018-09-25","proceeding":"NAACL 2019 6","authors":["Abdalghani Abujabal","Rishiraj Saha Roy","Mohamed Yahya","Gerhard Weikum"],"abstract":"To bridge the gap between the capabilities of the state-of-the-art in factoid\nquestion answering (QA) and what users ask, we need large datasets of real user\nquestions that capture the various question phenomena users are interested in,\nand the diverse ways in which these questions are formulated. We introduce\nComQA, a large dataset of real user questions that exhibit different\nchallenging aspects such as compositionality, temporal reasoning, and\ncomparisons. ComQA questions come from the WikiAnswers community QA platform,\nwhich typically contains questions that are not satisfactorily answerable by\nexisting search engine technology. Through a large crowdsourcing effort, we\nclean the question dataset, group questions into paraphrase clusters, and\nannotate clusters with their answers. ComQA contains 11,214 questions grouped\ninto 4,834 paraphrase clusters. We detail the process of constructing ComQA,\nincluding the measures taken to ensure its high quality while making effective\nuse of crowdsourcing. We also present an extensive analysis of the dataset and\nthe results achieved by state-of-the-art systems on ComQA, demonstrating that\nour dataset can be a driver of future research on QA.","url_abs":"http://arxiv.org/abs/1809.09528v2","url_pdf":"http://arxiv.org/pdf/1809.09528v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"question-answering","task_name":"Question Answering"}],"methods":[],"datasets_introduced":[{"slug":"comqa","name":"ComQA","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1809.09528","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}