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 source-channel 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.
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Gao, W., Wong, KF., Lam, W. (2005). Improving Transliteration with Precise Alignment of Phoneme Chunks and Using Contextual Features. In: Myaeng, S.H., Zhou, M., Wong, KF., Zhang, HJ. (eds) Information Retrieval Technology. AIRS 2004. Lecture Notes in Computer Science, vol 3411. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31871-2_10
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DOI: https://doi.org/10.1007/978-3-540-31871-2_10
Publisher Name: Springer, Berlin, Heidelberg
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