Phonology-Augmented Statistical Framework for Machine Transliteration Using Limited Linguistic Resources

Phonology-Augmented Statistical Framework for Machine Transliteration Using Limited Linguistic Resources
Title:
Phonology-Augmented Statistical Framework for Machine Transliteration Using Limited Linguistic Resources
Other Titles:
IEEE/ACM Transactions on Audio, Speech, and Language Processing
Keywords:
Publication Date:
10 October 2018
Citation:
G. H. Ngo, M. Nguyen and N. F. Chen, "Phonology-Augmented Statistical Framework for Machine Transliteration Using Limited Linguistic Resources," in IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 27, no. 1, pp. 199-211, Jan. 2019. doi: 10.1109/TASLP.2018.2875269
Abstract:
Transliteration converts words in a source language (e.g., English) into words in a target language (e.g., Vietnamese). This conversion considers the phonological structure of the target language, as the transliterated output needs to be pronounceable in the target language. For example, a word in Vietnamese that begins with a consonant cluster is phonologically invalid and thus would be an incorrect output of a transliteration system. Most statistical transliteration approaches, albeit being widely adopted, do not explicitly model the target language's phonology, which often results in invalid outputs. The problem is compounded by the limited linguistic resources available when converting foreign words to transliterated words in the target language. In this paper, we present a phonology-augmented statistical framework suitable for transliteration, especially when only limited linguistic resources are available. We propose the concept of pseudo-syllables as structures representing how segments of a foreign word are organized according to the syllables of the target language's phonology. We performed transliteration experiments on Vietnamese and Cantonese. We show that the proposed framework outperforms the statistical baseline by up to 44.68% relative, when there are limited training examples (587 entries).
License type:
PublisherCopyrights
Funding Info:
Description:
(c) 2018 IEEE.
ISSN:
2329-9290
2329-9304
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