Nonparametric Bayesian machine transliteration with synchronous adaptor grammars

Abstract

Machine transliteration is defined as automatic phonetic translation of names across languages. In this paper, we propose synchronous adaptor grammar, a novel nonparametric Bayesian learning approach, for machine transliteration. This model provides a general framework without heuristic or restriction to automatically learn syllable equivalents between languages. The proposed model outperforms the state-of-the-art EMbased model in the English to Chinese transliteration task. © 2011 Association for Computational Linguistics.