{"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/towards-end-to-end-learning-for-dialog-state","title":"Towards End-to-End Learning for Dialog State Tracking and Management using Deep Reinforcement Learning","arxiv_id":"1606.02560","date":"2016-06-08","proceeding":"WS 2016 9","authors":["Tiancheng Zhao","Maxine Eskenazi"],"abstract":"This paper presents an end-to-end framework for task-oriented dialog systems\nusing a variant of Deep Recurrent Q-Networks (DRQN). The model is able to\ninterface with a relational database and jointly learn policies for both\nlanguage understanding and dialog strategy. Moreover, we propose a hybrid\nalgorithm that combines the strength of reinforcement learning and supervised\nlearning to achieve faster learning speed. We evaluated the proposed model on a\n20 Question Game conversational game simulator. Results show that the proposed\nmethod outperforms the modular-based baseline and learns a distributed\nrepresentation of the latent dialog state.","url_abs":"http://arxiv.org/abs/1606.02560v2","url_pdf":"http://arxiv.org/pdf/1606.02560v2.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":"towards-end-to-end-learning-for-dialog-state","repo_url":"https://github.com/snakeztc/NeuralDialog-DM","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"deep-reinforcement-learning","task_name":"Deep Reinforcement Learning"},{"task_slug":"management","task_name":"Management"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":null,"task_name":"dialog state tracking"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1606.02560","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}