A noisy channel model for grapheme-based machine transliteration
Yuxiang Jia, Danqing Zhu, Shiwen Yu · 2009
Machine transliteration is an important Natural Language Processing task. This paper proposes a Noisy Channel Model for Grapheme-based machine transliteration. Moses, a phrase-based Statistical Machine Translation tool, is employed for the implementation of the system. Experiments are carried out on the NEWS 2009 Machine Transliteration Shared Task English-Chinese track. English-Chinese back transliteration is studied as well.