Name-aware language model adaptation and sparse features for statistical machine translation

Wen Wang, Haibo Li, Heng Ji

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We propose approaches improving statistical machine translation (SMT) performance, by developing name-aware language model adaptations and sparse features, in addition to extracting name-aware translation grammar and rules, adding name phrase table, and name translation driven decoding. Chinese-English translation experiments showed that our proposed approaches produce an absolute gain of +2.3 BLEU on top of our previous high-performing, name-aware machine translation system.

Original languageEnglish (US)
Title of host publication2015 IEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2015 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages324-330
Number of pages7
ISBN (Electronic)9781479972913
DOIs
StatePublished - Feb 10 2016
Externally publishedYes
EventIEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2015 - Scottsdale, United States
Duration: Dec 13 2015Dec 17 2015

Publication series

Name2015 IEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2015 - Proceedings

Other

OtherIEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2015
CountryUnited States
CityScottsdale
Period12/13/1512/17/15

Keywords

  • language model adaptation
  • name translation
  • sparse features
  • statistical machine translation

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition

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