{"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/reinforcement-learning-for-bandit-neural","title":"Reinforcement Learning for Bandit Neural Machine Translation with Simulated Human Feedback","arxiv_id":"1707.07402","date":"2017-07-24","proceeding":"EMNLP 2017 9","authors":["Khanh Nguyen","Hal Daumé III","Jordan Boyd-Graber"],"abstract":"Machine translation is a natural candidate problem for reinforcement learning\nfrom human feedback: users provide quick, dirty ratings on candidate\ntranslations to guide a system to improve. Yet, current neural machine\ntranslation training focuses on expensive human-generated reference\ntranslations. We describe a reinforcement learning algorithm that improves\nneural machine translation systems from simulated human feedback. Our algorithm\ncombines the advantage actor-critic algorithm (Mnih et al., 2016) with the\nattention-based neural encoder-decoder architecture (Luong et al., 2015). This\nalgorithm (a) is well-designed for problems with a large action space and\ndelayed rewards, (b) effectively optimizes traditional corpus-level machine\ntranslation metrics, and (c) is robust to skewed, high-variance, granular\nfeedback modeled after actual human behaviors.","url_abs":"http://arxiv.org/abs/1707.07402v4","url_pdf":"http://arxiv.org/pdf/1707.07402v4.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":"reinforcement-learning-for-bandit-neural","repo_url":"https://github.com/khanhptnk/bandit-nmt","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"machine-translation","task_name":"Machine Translation"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"translation","task_name":"Translation"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1707.07402","atlas_url":"https://app.syntology.ai/?focus=1707.07402","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}