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Nonparametric Bayesian machine transliteration with synchronous adaptor grammars

  • Yun Huang*
  • , Min Zhang
  • , Chew Lim Tan
  • *Corresponding author for this work
  • Agency for Science, Technology and Research, Singapore
  • National University of Singapore

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Machine transliteration is defined as automatic phonetic translation of names across languages. In this paper, we propose synchronous adaptor grammar, a novel nonparametric Bayesian learning approach, for machine transliteration. This model provides a general framework without heuristic or restriction to automatically learn syllable equivalents between languages. The proposed model outperforms the state-of-the-art EMbased model in the English to Chinese transliteration task.

Original languageEnglish
Title of host publicationACL-HLT 2011 - Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics
Subtitle of host publicationHuman Language Technologies
Pages534-539
Number of pages6
StatePublished - 2011
Externally publishedYes
Event49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, ACL-HLT 2011 - Portland, OR, United States
Duration: 19 Jun 201124 Jun 2011

Publication series

NameACL-HLT 2011 - Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies
Volume2

Conference

Conference49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, ACL-HLT 2011
Country/TerritoryUnited States
CityPortland, OR
Period19/06/1124/06/11

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