Syllable-based machine transliteration with extra phrase features

Authors:
Chunyue Zhang;Tingting Li;Tiejun Zhao
Affiliations:
Harbin Institute of Technology, Harbin, China;Harbin Institute of Technology, Harbin, China;Harbin Institute of Technology, Harbin, China
Venue:
NEWS '12 Proceedings of the 4th Named Entity Workshop
Year:
2012

Citing 9
Cited 1

A systematic comparison of various statistical alignment models

Computational Linguistics
Machine transliteration

Computational Linguistics
A joint source-channel model for machine transliteration

ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Moses: open source toolkit for statistical machine translation

ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
Named entity translation with web mining and transliteration

IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A noisy channel model for grapheme-based machine transliteration

NEWS '09 Proceedings of the 2009 Named Entities Workshop: Shared Task on Transliteration
Jointly optimizing a two-step conditional random field model for machine transliteration and its fast decoding algorithm

ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
Machine transliteration: leveraging on third languages

COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Nonparametric Bayesian machine transliteration with synchronous adaptor grammars

HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2

Report of NEWS 2012 machine transliteration shared task

NEWS '12 Proceedings of the 4th Named Entity Workshop

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper describes our syllable-based phrase transliteration system for the NEWS 2012 shared task on English-Chinese track and its back. Grapheme-based Transliteration maps the character(s) in the source side to the target character(s) directly. However, character-based segmentation on English side will cause ambiguity in alignment step. In this paper we utilize Phrase-based model to solve machine transliteration with the mapping between Chinese characters and English syllables rather than English characters. Two heuristic rule-based syllable segmentation algorithms are applied. This transliteration model also incorporates three phonetic features to enhance discriminative ability for phrase. The primary system achieved 0.330 on Chinese-English and 0.177 on English-Chinese in terms of top-1 accuracy.