{"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/multi-source-neural-translation","title":"Multi-Source Neural Translation","arxiv_id":"1601.00710","date":"2016-01-05","proceeding":"NAACL 2016 6","authors":["Barret Zoph","Kevin Knight"],"abstract":"We build a multi-source machine translation model and train it to maximize\nthe probability of a target English string given French and German sources.\nUsing the neural encoder-decoder framework, we explore several combination\nmethods and report up to +4.8 Bleu increases on top of a very strong\nattention-based neural translation model.","url_abs":"http://arxiv.org/abs/1601.00710v1","url_pdf":"http://arxiv.org/pdf/1601.00710v1.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":"multi-source-neural-translation","repo_url":"https://github.com/isi-nlp/Zoph_RNN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"machine-translation","task_name":"Machine Translation"},{"task_slug":"translation","task_name":"Translation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1601.00710","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}