Sentence level dialect identification for machine translation system selection

Wael Salloum, Heba Elfardy, Linda Alamir-Salloum, Nizar Habash, Mona Diab

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

Abstract

In this paper we study the use of sentence-level dialect identification in optimizing machine translation system selection when translating mixed dialect input. We test our approach on Arabic, a prototypical diglossic language; and we optimize the combination of four different machine translation systems. Our best result improves over the best single MT system baseline by 1.0% BLEU and over a strong system selection baseline by 0.6% BLEU on a blind test set.

Original languageEnglish (US)
Title of host publicationLong Papers
PublisherAssociation for Computational Linguistics (ACL)
Pages772-778
Number of pages7
ISBN (Print)9781937284732
DOIs
StatePublished - 2014
Event52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014 - Baltimore, MD, United States
Duration: Jun 22 2014Jun 27 2014

Publication series

Name52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014 - Proceedings of the Conference
Volume2

Other

Other52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014
CountryUnited States
CityBaltimore, MD
Period6/22/146/27/14

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language

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  • Cite this

    Salloum, W., Elfardy, H., Alamir-Salloum, L., Habash, N., & Diab, M. (2014). Sentence level dialect identification for machine translation system selection. In Long Papers (pp. 772-778). (52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014 - Proceedings of the Conference; Vol. 2). Association for Computational Linguistics (ACL). https://doi.org/10.3115/v1/p14-2125