{"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/modeling-past-and-future-for-neural-machine","title":"Modeling Past and Future for Neural Machine Translation","arxiv_id":"1711.09502","date":"2017-11-27","proceeding":"TACL 2018 1","authors":["Zaixiang Zheng","Hao Zhou","Shu-Jian Huang","Lili Mou","Xin-yu Dai","Jia-Jun Chen","Zhaopeng Tu"],"abstract":"Existing neural machine translation systems do not explicitly model what has\nbeen translated and what has not during the decoding phase. To address this\nproblem, we propose a novel mechanism that separates the source information\ninto two parts: translated Past contents and untranslated Future contents,\nwhich are modeled by two additional recurrent layers. The Past and Future\ncontents are fed to both the attention model and the decoder states, which\noffers NMT systems the knowledge of translated and untranslated contents.\nExperimental results show that the proposed approach significantly improves\ntranslation performance in Chinese-English, German-English and English-German\ntranslation tasks. Specifically, the proposed model outperforms the\nconventional coverage model in both of the translation quality and the\nalignment error rate.","url_abs":"http://arxiv.org/abs/1711.09502v2","url_pdf":"http://arxiv.org/pdf/1711.09502v2.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":"modeling-past-and-future-for-neural-machine","repo_url":"https://github.com/zhengzx-nlp/past-and-future-nmt","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"machine-translation","task_name":"Machine Translation"},{"task_slug":"nmt","task_name":"NMT"},{"task_slug":"translation","task_name":"Translation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1711.09502","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}