{"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/answerer-in-questioners-mind-information","title":"Answerer in Questioner's Mind: Information Theoretic Approach to Goal-Oriented Visual Dialog","arxiv_id":"1802.03881","date":"2018-02-12","proceeding":"NeurIPS 2018 12","authors":["Sang-Woo Lee","Yu-Jung Heo","Byoung-Tak Zhang"],"abstract":"Goal-oriented dialog has been given attention due to its numerous\napplications in artificial intelligence. Goal-oriented dialogue tasks occur\nwhen a questioner asks an action-oriented question and an answerer responds\nwith the intent of letting the questioner know a correct action to take. To ask\nthe adequate question, deep learning and reinforcement learning have been\nrecently applied. However, these approaches struggle to find a competent\nrecurrent neural questioner, owing to the complexity of learning a series of\nsentences. Motivated by theory of mind, we propose \"Answerer in Questioner's\nMind\" (AQM), a novel information theoretic algorithm for goal-oriented dialog.\nWith AQM, a questioner asks and infers based on an approximated probabilistic\nmodel of the answerer. The questioner figures out the answerer's intention via\nselecting a plausible question by explicitly calculating the information gain\nof the candidate intentions and possible answers to each question. We test our\nframework on two goal-oriented visual dialog tasks: \"MNIST Counting Dialog\" and\n\"GuessWhat?!\". In our experiments, AQM outperforms comparative algorithms by a\nlarge margin.","url_abs":"http://arxiv.org/abs/1802.03881v3","url_pdf":"http://arxiv.org/pdf/1802.03881v3.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":"answerer-in-questioners-mind-information","repo_url":"https://github.com/naver/aqm-plus","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"goal-oriented-dialog","task_name":"Goal-Oriented Dialog"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"visual-dialogue","task_name":"Visual Dialog"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1802.03881","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}