{"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/abg-coqa-clarifying-ambiguity-in","title":"Abg-CoQA: Clarifying Ambiguity in Conversational Question Answering","arxiv_id":null,"date":"2021-06-22","proceeding":"AKBC 2021 10","authors":["Meiqi Guo","Mingda Zhang","Siva Reddy","Malihe Alikhani"],"abstract":"Effective  communication  is  about  the  dissemination  of  properly  worded  meaningful ideas/messages that are comprehensible to both sender and receiver and which ultimately can attract the desired response or feedback.  For machines to engage in a conversation, it is therefore essential to enable them to clarify ambiguity and achieve a common ground.  We introduce Abg-CoQA, a novel dataset for clarifying ambiguity in Conversational Question Answering systems.  Our dataset contains 9k questions with answers where 1k questions are ambiguous, obtained from 4k text passages from five diverse domains.  For ambiguous questions,  a  clarification  conversational  turn  is  collected.   We  evaluate  strong  language generation models and conversational question answering models on Abg-CoQA. The best-performing system achieves a BLEU-1 score of 12.9% on generating clarification question, which  is  27.9  points  behind  human  performance  (40.8%);  and  a  F1  score  of  40.1%  on question  answering  after  clarification,  which  is  35.1  points  behind  human  performance (75.2%), indicating there is ample room for improvement.","url_abs":"https://openreview.net/forum?id=SlDZ1o8FsJU","url_pdf":"https://openreview.net/pdf?id=SlDZ1o8FsJU","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":"abg-coqa-clarifying-ambiguity-in","repo_url":"https://github.com/meiqiguo/akbc2021-abg-coqa","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"4k","task_name":"4k"},{"task_slug":"conversational-question-answering","task_name":"Conversational Question Answering"},{"task_slug":"question-answering","task_name":"Question Answering"},{"task_slug":"text-generation","task_name":"Text Generation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}