{"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/deep-reinforcement-learning-for-dialogue","title":"Deep Reinforcement Learning for Dialogue Generation","arxiv_id":"1606.01541","date":"2016-06-05","proceeding":"EMNLP 2016 11","authors":["Jiwei Li","Will Monroe","Alan Ritter","Michel Galley","Jianfeng Gao","Dan Jurafsky"],"abstract":"Recent neural models of dialogue generation offer great promise for\ngenerating responses for conversational agents, but tend to be shortsighted,\npredicting utterances one at a time while ignoring their influence on future\noutcomes. Modeling the future direction of a dialogue is crucial to generating\ncoherent, interesting dialogues, a need which led traditional NLP models of\ndialogue to draw on reinforcement learning. In this paper, we show how to\nintegrate these goals, applying deep reinforcement learning to model future\nreward in chatbot dialogue. The model simulates dialogues between two virtual\nagents, using policy gradient methods to reward sequences that display three\nuseful conversational properties: informativity (non-repetitive turns),\ncoherence, and ease of answering (related to forward-looking function). We\nevaluate our model on diversity, length as well as with human judges, showing\nthat the proposed algorithm generates more interactive responses and manages to\nfoster a more sustained conversation in dialogue simulation. This work marks a\nfirst step towards learning a neural conversational model based on the\nlong-term success of dialogues.","url_abs":"http://arxiv.org/abs/1606.01541v4","url_pdf":"http://arxiv.org/pdf/1606.01541v4.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":"deep-reinforcement-learning-for-dialogue","repo_url":"https://github.com/LiangqunLu/DLForChatbot","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"deep-reinforcement-learning-for-dialogue","repo_url":"https://github.com/liuyuemaicha/Deep-Reinforcement-Learning-for-Dialogue-Generation-in-tensorflow","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"deep-reinforcement-learning-for-dialogue","repo_url":"https://github.com/tfolkman/deep-learning-experiments","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"deep-reinforcement-learning-for-dialogue","repo_url":"https://github.com/zhanghainan/RNN-S2S","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"deep-reinforcement-learning-for-dialogue","repo_url":"https://github.com/zhanghainan/RNN-encdec","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"deep-reinforcement-learning-for-dialogue","repo_url":"https://github.com/zhanghainan/ReinforcingCoherence","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"deep-reinforcement-learning-for-dialogue","repo_url":"https://github.com/zhanghainan/TailoredSeq2Seq2DifferentConversationScenarios","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"deep-reinforcement-learning-for-dialogue","repo_url":"https://github.com/zhanghainan/s2s-with-attention","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"chatbot","task_name":"Chatbot"},{"task_slug":"deep-reinforcement-learning","task_name":"Deep Reinforcement Learning"},{"task_slug":"dialogue-generation","task_name":"Dialogue Generation"},{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"policy-gradient-methods","task_name":"Policy Gradient Methods"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1606.01541","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}