{"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/unsupervised-neural-machine-translation-with","title":"Unsupervised Neural Machine Translation with SMT as Posterior Regularization","arxiv_id":"1901.04112","date":"2019-01-14","proceeding":null,"authors":["Shuo Ren","Zhirui Zhang","Shujie Liu","Ming Zhou","Shuai Ma"],"abstract":"Without real bilingual corpus available, unsupervised Neural Machine\nTranslation (NMT) typically requires pseudo parallel data generated with the\nback-translation method for the model training. However, due to weak\nsupervision, the pseudo data inevitably contain noises and errors that will be\naccumulated and reinforced in the subsequent training process, leading to bad\ntranslation performance. To address this issue, we introduce phrase based\nStatistic Machine Translation (SMT) models which are robust to noisy data, as\nposterior regularizations to guide the training of unsupervised NMT models in\nthe iterative back-translation process. Our method starts from SMT models built\nwith pre-trained language models and word-level translation tables inferred\nfrom cross-lingual embeddings. Then SMT and NMT models are optimized jointly\nand boost each other incrementally in a unified EM framework. In this way, (1)\nthe negative effect caused by errors in the iterative back-translation process\ncan be alleviated timely by SMT filtering noises from its phrase tables;\nmeanwhile, (2) NMT can compensate for the deficiency of fluency inherent in\nSMT. Experiments conducted on en-fr and en-de translation tasks show that our\nmethod outperforms the strong baseline and achieves new state-of-the-art\nunsupervised machine translation performance.","url_abs":"http://arxiv.org/abs/1901.04112v1","url_pdf":"http://arxiv.org/pdf/1901.04112v1.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":"unsupervised-neural-machine-translation-with","repo_url":"https://github.com/Imagist-Shuo/UNMT-SPR","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"machine-translation","task_name":"Machine Translation"},{"task_slug":"nmt","task_name":"NMT"},{"task_slug":"translation","task_name":"Translation"},{"task_slug":"unsupervised-machine-translation","task_name":"Unsupervised Machine Translation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/unsupervised-machine-translation-on-wmt2014-2","task":"Unsupervised Machine Translation","dataset":"WMT2014 English-French","model":"SMT as posterior regularization","rank_in_archive_order":6,"of":7,"metrics":{"BLEU":"29.5"},"uses_additional_data":false},{"leaderboard":"/sota/unsupervised-machine-translation-on-wmt2014","task":"Unsupervised Machine Translation","dataset":"WMT2014 English-German","model":"SMT as posterior regularization","rank_in_archive_order":2,"of":2,"metrics":{"BLEU":"17.0"},"uses_additional_data":false},{"leaderboard":"/sota/unsupervised-machine-translation-on-wmt2014-1","task":"Unsupervised Machine Translation","dataset":"WMT2014 French-English","model":"SMT as posterior regularization","rank_in_archive_order":5,"of":7,"metrics":{"BLEU":"28.9"},"uses_additional_data":false},{"leaderboard":"/sota/unsupervised-machine-translation-on-wmt2014-3","task":"Unsupervised Machine Translation","dataset":"WMT2014 German-English","model":"SMT as posterior regularization","rank_in_archive_order":2,"of":2,"metrics":{"BLEU":"20.4"},"uses_additional_data":false},{"leaderboard":"/sota/unsupervised-machine-translation-on-wmt2016","task":"Unsupervised Machine Translation","dataset":"WMT2016 English-German","model":"SMT as posterior regularization","rank_in_archive_order":5,"of":7,"metrics":{"BLEU":"21.7"},"uses_additional_data":false},{"leaderboard":"/sota/unsupervised-machine-translation-on-wmt2016-1","task":"Unsupervised Machine Translation","dataset":"WMT2016 German-English","model":"SMT as posterior regularization","rank_in_archive_order":6,"of":7,"metrics":{"BLEU":"26.3"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1901.04112","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1901.04112"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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