Nonparametric Bayesian Machine Transliteration with Synchronous Adaptor Grammars
نویسندگان
چکیده
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.
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