{"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/scalable-machine-translation-in-memory","title":"Scalable Machine Translation in Memory Constrained Environments","arxiv_id":"1610.02003","date":"2016-10-06","proceeding":null,"authors":["Paul Baltescu"],"abstract":"Machine translation is the discipline concerned with developing automated\ntools for translating from one human language to another. Statistical machine\ntranslation (SMT) is the dominant paradigm in this field. In SMT, translations\nare generated by means of statistical models whose parameters are learned from\nbilingual data. Scalability is a key concern in SMT, as one would like to make\nuse of as much data as possible to train better translation systems.\n  In recent years, mobile devices with adequate computing power have become\nwidely available. Despite being very successful, mobile applications relying on\nNLP systems continue to follow a client-server architecture, which is of\nlimited use because access to internet is often limited and expensive. The goal\nof this dissertation is to show how to construct a scalable machine translation\nsystem that can operate with the limited resources available on a mobile\ndevice.\n  The main challenge for porting translation systems on mobile devices is\nmemory usage. The amount of memory available on a mobile device is far less\nthan what is typically available on the server side of a client-server\napplication. In this thesis, we investigate alternatives for the two components\nwhich prevent standard translation systems from working on mobile devices due\nto high memory usage. We show that once these standard components are replaced\nwith our proposed alternatives, we obtain a scalable translation system that\ncan work on a device with limited memory.","url_abs":"http://arxiv.org/abs/1610.02003v1","url_pdf":"http://arxiv.org/pdf/1610.02003v1.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":"scalable-machine-translation-in-memory","repo_url":"https://github.com/redpony/cdec","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","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":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}