Improving machine transliteration performance by using multiple transliteration models

Abstract

Machine transliteration has received significant attention as a supporting tool for machine translation and cross-language information retrieval. During the last decade, four kinds of transliteration model have been studied - grapheme-based model, phoneme-based model, hybrid model, and correspondence-based model. These models are classified in terms of the information sources for transliteration or the units to be transliterated - source graphemes, source phonemes, both source graphemes and source phonemes, and the correspondence between source graphemes and phonemes, respectively. Although each transliteration model has shown relatively good performance, one model alone has limitations on handling complex transliteration behaviors. To address the problem, we combined different transliteration models with a