{"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/multispanqa-a-dataset-for-multi-span-question","title":"MultiSpanQA: A Dataset for Multi-Span Question Answering","arxiv_id":null,"date":"2022-07-01","proceeding":"NAACL 2022 7","authors":["Haonan Li","Martin Tomko","Maria Vasardani","Timothy Baldwin"],"abstract":"Most existing reading comprehension datasets focus on single-span answers, which can be extracted as a single contiguous span from a given text passage. Multi-span questions, i.e., questions whose answer is a series of multiple discontiguous spans in the text, are common real life but are less studied. In this paper, we present MultiSpanQA, a new dataset that focuses on multi-span questions. Raw questions and contexts are extracted from the Natural Questions dataset. After multi-span re-annotation, MultiSpanQA consists of over a total of 6,000 multi-span questions in the basic version, and over 19,000 examples with unanswerable questions, and questions with single-, and multi-span answers in the expanded version. We introduce new metrics for the purposes of multi-span question answering evaluation, and establish several baselines using advanced models. Finally, we propose a new model which beats all baselines and achieves state-of-the-art on our dataset.","url_abs":"https://aclanthology.org/2022.naacl-main.90","url_pdf":"https://aclanthology.org/2022.naacl-main.90.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":[{"paper_slug":"multispanqa-a-dataset-for-multi-span-question","repo_url":"https://github.com/haonan-li/MultiSpanQA","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"natural-questions","task_name":"Natural Questions"},{"task_slug":"question-answering","task_name":"Question Answering"},{"task_slug":"reading-comprehension","task_name":"Reading Comprehension"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}