Transliteration of Name Entity via Improved Statistical Translation on Character Sequences

Abstract

Transliteration of given parallel name entities can be formulated as a phrase-based statistical machine translation (SMT) process, via its routine procedure comprising training, optimization and decoding. In this paper, we present our approach to transliterating name entities using the loglinear phrase-based SMT on character sequences. Our proposed work improves the translation by using bidirectional models, plus some heuristic guidance integrated in the decoding process. Our evaluated results indicate that this approach performs well in all standard runs in the NEWS2009 Machine Transliteration Shared Task.