A joint source-channel model for machine transliteration

  • Authors:

  • Li Haizhou;Zhang Min;Su Jian

  • Affiliations:

  • Institute for Infocomm Research, Singapore;Institute for Infocomm Research, Singapore;Institute for Infocomm Research, Singapore

  • Venue:
  • ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
  • Year:
  • 2004

Quantified Score

Hi-index 0.00

Visualization

Abstract

Most foreign names are transliterated into Chinese, Japanese or Korean with approximate phonetic equivalents. The transliteration is usually achieved through intermediate phonemic mapping. This paper presents a new framework that allows direct orthographical mapping (DOM) between two different languages, through a joint source-channel model, also called n-gram transliteration model (TM). With the n-gram TM model, we automate the orthographic alignment process to derive the aligned transliteration units from a bilingual dictionary. The n-gram TM under the DOM framework greatly reduces system development effort and provides a quantum leap in improvement in transliteration accuracy over that of other state-of-the-art machine learning algorithms. The modeling framework is validated through several experiments for English-Chinese language pair.