{"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/a-hybrid-approach-for-hindi-english-machine","title":"A Hybrid Approach For Hindi-English Machine Translation","arxiv_id":"1702.01587","date":"2017-02-06","proceeding":null,"authors":["Omkar Dhariya","Shrikant Malviya","Uma Shanker Tiwary"],"abstract":"In this paper, an extended combined approach of phrase based statistical\nmachine translation (SMT), example based MT (EBMT) and rule based MT (RBMT) is\nproposed to develop a novel hybrid data driven MT system capable of\noutperforming the baseline SMT, EBMT and RBMT systems from which it is derived.\nIn short, the proposed hybrid MT process is guided by the rule based MT after\ngetting a set of partial candidate translations provided by EBMT and SMT\nsubsystems. Previous works have shown that EBMT systems are capable of\noutperforming the phrase-based SMT systems and RBMT approach has the strength\nof generating structurally and morphologically more accurate results. This\nhybrid approach increases the fluency, accuracy and grammatical precision which\nimprove the quality of a machine translation system. A comparison of the\nproposed hybrid machine translation (HTM) model with renowned translators i.e.\nGoogle, BING and Babylonian is also presented which shows that the proposed\nmodel works better on sentences with ambiguity as well as comprised of idioms\nthan others.","url_abs":"http://arxiv.org/abs/1702.01587v1","url_pdf":"http://arxiv.org/pdf/1702.01587v1.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":"a-hybrid-approach-for-hindi-english-machine","repo_url":"https://github.com/PranotiDesai/MachineTranslation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","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}