Maximum n-Gram HMM-based Name Transliteration: Experiment in NEWS 2009 on English-Chinese Corpus

Abstract

We propose an English-Chinese name transliteration system using a maximum N-gram Hidden Markov Model. To handle special challenges with alphabet-based and character-based language pair, we apply a two-phase transliteration model by building two HMM models, one between English and Chinese Pinyin and another between Chinese Pinyin and Chinese characters. Our model improves traditional HMM by assigning the longest prior translation sequence of syllables the largest weight. In our non-standard runs, we use a Web-mining module to boost the performance by adding online popularity information of candidate translations. The entire model does not rely on any dictionaries and the probability tables are derived merely from training corpus. In participation of NEWS 2009 experiment, our model achieved 0.462 Top-1 accuracy and 0.764 Mean F-score.