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We use a Phrase-Based Statistical Machine Translation approach to Transliteration where the words are replaced by characters and sentences by words. We employ the standard SMT tools like GIZA++ for learning alignments and Moses for learning the phrase tables and decoding. Besides tuning the standard SMT parameters, we focus on tuning the Character Sequence Model (CSM) related parameters like order of the CSM, weight assigned to CSM during decoding and corpus used for CSM estimation. Our results show that paying sufficient attention to CSM pays off in terms of increased transliteration accuracies.

Experiences with English-Hindi, English-Tamil and English-Kannada transliteration tasks at NEWS 2009