{"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/selqa-a-new-benchmark-for-selection-based","title":"SelQA: A New Benchmark for Selection-based Question Answering","arxiv_id":"1606.08513","date":"2016-06-27","proceeding":null,"authors":["Tomasz Jurczyk","Michael Zhai","Jinho D. Choi"],"abstract":"This paper presents a new selection-based question answering dataset, SelQA.\nThe dataset consists of questions generated through crowdsourcing and sentence\nlength answers that are drawn from the ten most prevalent topics in the English\nWikipedia. We introduce a corpus annotation scheme that enhances the generation\nof large, diverse, and challenging datasets by explicitly aiming to reduce word\nco-occurrences between the question and answers. Our annotation scheme is\ncomposed of a series of crowdsourcing tasks with a view to more effectively\nutilize crowdsourcing in the creation of question answering datasets in various\ndomains. Several systems are compared on the tasks of answer sentence selection\nand answer triggering, providing strong baseline results for future work to\nimprove upon.","url_abs":"http://arxiv.org/abs/1606.08513v3","url_pdf":"http://arxiv.org/pdf/1606.08513v3.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":"selqa-a-new-benchmark-for-selection-based","repo_url":"https://github.com/emorynlp/selqa","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"question-answering","task_name":"Question Answering"},{"task_slug":"sentence","task_name":"Sentence"}],"methods":[],"datasets_introduced":[{"slug":"selqa","name":"SelQA","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1606.08513","atlas_url":"https://app.syntology.ai/?focus=1606.08513","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}