Integrating an Unsupervised Transliteration Model into Statistical Machine Translation

Nadir Durrani, Hassan Sajjad, Hieu Hoang, Philipp Koehn

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

We investigate three methods for integrating an unsupervised transliteration model into an end-to-end SMT system. We induce a transliteration model from parallel data and use it to translate OOV words. Our approach is fully unsupervised and language independent. In the methods to integrate transliterations, we observed
improvements from 0.23-0.75 (∆ 0.41) BLEU points across 7 language pairs. We
also show that our mined transliteration corpora provide better rule coverage and
translation quality compared to the gold standard transliteration corpora.
Original languageEnglish
Title of host publicationProceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2014, April 26-30, 2014, Gothenburg, Sweden
Number of pages6
Publication statusPublished - 2014


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