Machine Transliteration using Target-Language Grapheme and Phoneme: Multi-engine Transliteration Approach

Abstract

This paper describes our approach to "NEWS 2009 Machine Transliteration Shared Task." We built multiple transliteration engines based on different combinations of two transliteration models and three machine learning algorithms. Then, the outputs from these transliteration engines were combined using re-ranking functions. Our method was applied to all language pairs in "NEWS 2009 Machine Transliteration Shared Task." The official results of our standard runs were ranked the best for four language pairs and the second best for three language pairs.