Improving Transliteration with Precise Alignment of Phoneme Chunks and Using Contextual Features

Abstract. Automatic transliteration of foreign names is basically regarded as a diminutive clone of the machine translation (MT) problem. It thus follows IBM’s conventional MT models under the sourcechannel framework. Nonetheless, some parameters of this model dealing with zero-fertility words in the target sequences, can negatively impact transliteration effectiveness because of the inevitable inverted conditional probability estimation. Instead of source-channel, this paper presents a direct probabilistic transliteration model using contextual features of phonemes with a tailored alignment scheme for phoneme chunks. Experiments demonstrate superior performance over the source-channel for the task of English-Chinese transliteration.

• Type: Conference
• Year: 2004
• Where: AIRS
• Authors: Wei Gao, Kam-Fai Wong, Wai Lam
• Keywords: AIRS 2004, Automatic Transliteration, English-Chinese Transliteration, Information Retrieval, Probabilistic Transliteration Model