Experimental studies using statistical algorithms on transliterating phoneme sequences for English-Chinese name translation

Publication Type

Journal Article

Publication Date

1-2006

Abstract

Machine transliteration is automatic generation of the phonetic equivalents in a target language given a source language term, which is useful in many cross language applications. Transliteration between far distant languages, e.g. English and Chinese, is challenging because their phonological dissimilarities are significant. Existing techniques are typically rule-based or statistically noisy channel-based. Their accuracies are very low due to their intrinsic limitations on modeling transcription details. We propose direct statistical approaches on transliterating phoneme sequences for English–Chinese name translation. Aiming to improve performance, we propose two direct models: First, we adopt Finite State Automata on a process of direct mapping from English phonemes to a set of rudimentary Chinese phonetic symbols plus mapping units dynamically discovered from training. An effective algorithm for aligning phoneme chunks is proposed. Second, contextual features of each phoneme are taken into consideration by means of Maximum Entropy formalism, and the model is further refined with the precise alignment scheme using phoneme chunks. We compare our approaches with the noisy channel baseline that applies IBM SMT model, and demonstrate their superiority.

Keywords

Machine Transliteration, Finite State Transducer, Noisy Channel, Maximum Entropy, Phoneme, Fertility, Evaluation

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

International Journal of Computer Processing of Oriental Languages

Volume

19

Issue

1

First Page

63

Last Page

88

Identifier

10.1142/S0219427906001396

Additional URL

https://doi.org/10.1142/S0219427906001396

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