{"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/improving-robustness-of-machine-translation","title":"Improving Robustness of Machine Translation with Synthetic Noise","arxiv_id":"1902.09508","date":"2019-02-25","proceeding":"NAACL 2019 6","authors":["Vaibhav  Vaibhav","Sumeet Singh","Craig Stewart","Graham Neubig"],"abstract":"Modern Machine Translation (MT) systems perform consistently well on clean,\nin-domain text. However most human generated text, particularly in the realm of\nsocial media, is full of typos, slang, dialect, idiolect and other noise which\ncan have a disastrous impact on the accuracy of output translation. In this\npaper we leverage the Machine Translation of Noisy Text (MTNT) dataset to\nenhance the robustness of MT systems by emulating naturally occurring noise in\notherwise clean data. Synthesizing noise in this manner we are ultimately able\nto make a vanilla MT system resilient to naturally occurring noise and\npartially mitigate loss in accuracy resulting therefrom.","url_abs":"http://arxiv.org/abs/1902.09508v2","url_pdf":"http://arxiv.org/pdf/1902.09508v2.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":"improving-robustness-of-machine-translation","repo_url":"https://github.com/MysteryVaibhav/robust_mtnt","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"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=1902.09508","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}