Experiences with English-Hindi, English-Tamil and English-Kannada Transliteration Tasks at NEWS 2009

We use a Phrase-Based Statistical Ma-chine Translation approach to Translitera-tion where the words are replaced by char-acters and sentences by words. We employ the standard SMT tools like GIZA++ for learning alignments and Moses for learn-ing the phrase tables and decoding. Be-sides tuning the standard SMT parame-ters, we focus on tuning the Character Se-quence 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 ac-curacies.