{"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/learning-end-to-end-goal-oriented-dialog-with","title":"Learning End-to-End Goal-Oriented Dialog with Multiple Answers","arxiv_id":"1808.09996","date":"2018-08-24","proceeding":"EMNLP 2018 10","authors":["Janarthanan Rajendran","Jatin Ganhotra","Satinder Singh","Lazaros Polymenakos"],"abstract":"In a dialog, there can be multiple valid next utterances at any point. The\npresent end-to-end neural methods for dialog do not take this into account.\nThey learn with the assumption that at any time there is only one correct next\nutterance. In this work, we focus on this problem in the goal-oriented dialog\nsetting where there are different paths to reach a goal. We propose a new\nmethod, that uses a combination of supervised learning and reinforcement\nlearning approaches to address this issue. We also propose a new and more\neffective testbed, permuted-bAbI dialog tasks, by introducing multiple valid\nnext utterances to the original-bAbI dialog tasks, which allows evaluation of\ngoal-oriented dialog systems in a more realistic setting. We show that there is\na significant drop in performance of existing end-to-end neural methods from\n81.5% per-dialog accuracy on original-bAbI dialog tasks to 30.3% on\npermuted-bAbI dialog tasks. We also show that our proposed method improves the\nperformance and achieves 47.3% per-dialog accuracy on permuted-bAbI dialog\ntasks.","url_abs":"http://arxiv.org/abs/1808.09996v1","url_pdf":"http://arxiv.org/pdf/1808.09996v1.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":"learning-end-to-end-goal-oriented-dialog-with","repo_url":"https://github.com/IBM/permuted-bAbI-dialog-tasks","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"goal-oriented-dialog","task_name":"Goal-Oriented Dialog"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":null,"task_name":"valid"}],"methods":[],"datasets_introduced":[{"slug":"permuted-babi-dialog-task","name":"Permuted bAbI dialog task","full_name":null}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}