Translation of unseen bigrams by analogy using an SVM classifier

Hao Wang, Lu Lyu, Yves Lepage

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

Abstract

Detecting language divergences and predicting possible sub-translations is one of the most essential issues in machine translation. Since the existence of translation divergences, it is impractical to straightforward translate from source sentence into target sentence while keeping the high degree of accuracy and without additional information. In this paper, we investigate the problem from an emerging and special point of view: bigrams and the corresponding translations. We first profile corpora and explore the constituents of bigrams in the source language. Then we translate unseen bigrams based on proportional analogy and filter the outputs using an Support Vector Machine (SVM) classifier. The experiment results also show that even a small set of features from analogous can provide meaningful information in translating by analogy.

Original languageEnglish
Title of host publication29th Pacific Asia Conference on Language, Information and Computation, PACLIC 2015
PublisherShanghai Jiao Tong University
Pages16-25
Number of pages10
Publication statusPublished - 2015
Event29th Pacific Asia Conference on Language, Information and Computation, PACLIC 2015 - Shanghai, China
Duration: 2015 Oct 302015 Nov 1

Other

Other29th Pacific Asia Conference on Language, Information and Computation, PACLIC 2015
CountryChina
CityShanghai
Period15/10/3015/11/1

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Linguistics and Language

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  • Cite this

    Wang, H., Lyu, L., & Lepage, Y. (2015). Translation of unseen bigrams by analogy using an SVM classifier. In 29th Pacific Asia Conference on Language, Information and Computation, PACLIC 2015 (pp. 16-25). Shanghai Jiao Tong University.