Analogy-based machine translation using secability

Tatsuya Kimura, Jin Matsuoka, Yusuke Nishikawa, Yves Lepage

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

Abstract

The problem of reordering remains the main problem in machine translation. Computing structures of sentences and the alignment of substructures is a way that has been proposed to solve this problem. We use secability to compute structures and show its effectiveness in an example-based machine translation.

Original languageEnglish
Title of host publicationProceedings - 2014 International Conference on Computational Science and Computational Intelligence, CSCI 2014
PublisherIEEE Computer Society
Pages297-298
Number of pages2
ISBN (Print)9781479930098
DOIs
Publication statusPublished - 2014
Event2014 International Conference on Computational Science and Computational Intelligence, CSCI 2014 - Las Vegas, NV, United States
Duration: 2014 Mar 102014 Mar 13

Publication series

NameProceedings - 2014 International Conference on Computational Science and Computational Intelligence, CSCI 2014
Volume2

Conference

Conference2014 International Conference on Computational Science and Computational Intelligence, CSCI 2014
CountryUnited States
CityLas Vegas, NV
Period14/3/1014/3/13

Keywords

  • Alignment
  • Example-based machine translation
  • Proportional analogy
  • Secability
  • Translation table

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics

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