{"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/sequence-to-sequence-neural-network-models","title":"Sequence-to-sequence neural network models for transliteration","arxiv_id":"1610.09565","date":"2016-10-29","proceeding":null,"authors":["Mihaela Rosca","Thomas Breuel"],"abstract":"Transliteration is a key component of machine translation systems and\nsoftware internationalization. This paper demonstrates that neural\nsequence-to-sequence models obtain state of the art or close to state of the\nart results on existing datasets. In an effort to make machine transliteration\naccessible, we open source a new Arabic to English transliteration dataset and\nour trained models.","url_abs":"http://arxiv.org/abs/1610.09565v1","url_pdf":"http://arxiv.org/pdf/1610.09565v1.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":"sequence-to-sequence-neural-network-models","repo_url":"https://github.com/googlei18n/transliteration","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-translation","task_name":"Machine Translation"},{"task_slug":"translation","task_name":"Translation"},{"task_slug":"transliteration","task_name":"Transliteration"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1610.09565","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}