Hindi-to-Urdu machine translation through transliteration

Nadir Durrani*, Hassan Sajjad, Alexander Fraser, Helmut Schmid

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

32 Citations (Scopus)

Abstract

We present a novel approach to integrate transliteration into Hindi-to-Urdu statistical machine translation. We propose two probabilistic models, based on conditional and joint probability formulations, that are novel solutions to the problem. Our models consider both transliteration and translation when translating a particular Hindi word given the context whereas in previous work transliteration is only used for translating OOV (out-of-vocabulary) words. We use transliteration as a tool for disambiguation of Hindi homonyms which can be both translated or transliterated or transliterated differently based on different contexts. We obtain final BLEU scores of 19.35 (conditional probability model) and 19.00 (joint probability model) as compared to 14.30 for a baseline phrase-based system and 16.25 for a system which transliterates OOV words in the baseline system. This indicates that transliteration is useful for more than only translating OOV words for language pairs like Hindi-Urdu.

Original languageEnglish
Title of host publicationACL 2010 - 48th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference
Pages465-474
Number of pages10
Publication statusPublished - 2010
Externally publishedYes
Event48th Annual Meeting of the Association for Computational Linguistics, ACL 2010 - Uppsala, Sweden
Duration: 11 Jul 201016 Jul 2010

Publication series

NameACL 2010 - 48th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference

Conference

Conference48th Annual Meeting of the Association for Computational Linguistics, ACL 2010
Country/TerritorySweden
CityUppsala
Period11/07/1016/07/10

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