{"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/a-user-simulator-for-task-completion","title":"A User Simulator for Task-Completion Dialogues","arxiv_id":"1612.05688","date":"2016-12-17","proceeding":null,"authors":["Xiujun Li","Zachary C. Lipton","Bhuwan Dhingra","Lihong Li","Jianfeng Gao","Yun-Nung Chen"],"abstract":"Despite widespread interests in reinforcement-learning for task-oriented\ndialogue systems, several obstacles can frustrate research and development\nprogress. First, reinforcement learners typically require interaction with the\nenvironment, so conventional dialogue corpora cannot be used directly. Second,\neach task presents specific challenges, requiring separate corpus of\ntask-specific annotated data. Third, collecting and annotating human-machine or\nhuman-human conversations for task-oriented dialogues requires extensive domain\nknowledge. Because building an appropriate dataset can be both financially\ncostly and time-consuming, one popular approach is to build a user simulator\nbased upon a corpus of example dialogues. Then, one can train reinforcement\nlearning agents in an online fashion as they interact with the simulator.\nDialogue agents trained on these simulators can serve as an effective starting\npoint. Once agents master the simulator, they may be deployed in a real\nenvironment to interact with humans, and continue to be trained online. To ease\nempirical algorithmic comparisons in dialogues, this paper introduces a new,\npublicly available simulation framework, where our simulator, designed for the\nmovie-booking domain, leverages both rules and collected data. The simulator\nsupports two tasks: movie ticket booking and movie seeking. Finally, we\ndemonstrate several agents and detail the procedure to add and test your own\nagent in the proposed framework.","url_abs":"http://arxiv.org/abs/1612.05688v3","url_pdf":"http://arxiv.org/pdf/1612.05688v3.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":"a-user-simulator-for-task-completion","repo_url":"https://github.com/MiuLab/UserSimulator","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"a-user-simulator-for-task-completion","repo_url":"https://github.com/Ambitioner-c/UserSimulator","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"a-user-simulator-for-task-completion","repo_url":"https://github.com/AtmaHou/UserSimulator","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"a-user-simulator-for-task-completion","repo_url":"https://github.com/MiuLab/TC-Bot","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"a-user-simulator-for-task-completion","repo_url":"https://github.com/Zhihan1996/User-sim","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"a-user-simulator-for-task-completion","repo_url":"https://github.com/dirtdust/TC-Bot-python3","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"a-user-simulator-for-task-completion","repo_url":"https://github.com/durashi/Dialog_policy_network_for_low_resource_setting","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"a-user-simulator-for-task-completion","repo_url":"https://github.com/jerrylsu/TC-Bot","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"a-user-simulator-for-task-completion","repo_url":"https://github.com/markWJJ/TC-Bot","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"a-user-simulator-for-task-completion","repo_url":"https://github.com/tanayz/TC-Bot-py3","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"task-oriented-dialogue-systems","task_name":"Task-Oriented Dialogue Systems"},{"task_slug":"user-simulation","task_name":"User Simulation"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1612.05688","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}