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
    Volume2
    ISBN (Print)9781937284732
    StatePublished - Jan 1 2014
    Event52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014 - Baltimore, MD, United States
    Duration: Jun 22 2014Jun 27 2014

    Other

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

    Fingerprint

    dialect
    Machine Translation System
    language
    Diglossic
    Blind Test
    Language
    Translating

    ASJC Scopus subject areas

    • Language and Linguistics
    • Linguistics and Language

    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 (Vol. 2, pp. 772-778). Association for Computational Linguistics (ACL).

    Sentence level dialect identification for machine translation system selection. / Salloum, Wael; Elfardy, Heba; Alamir-Salloum, Linda; Habash, Nizar; Diab, Mona.

    Long Papers. Vol. 2 Association for Computational Linguistics (ACL), 2014. p. 772-778.

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

    Salloum, W, Elfardy, H, Alamir-Salloum, L, Habash, N & Diab, M 2014, Sentence level dialect identification for machine translation system selection. in Long Papers. vol. 2, Association for Computational Linguistics (ACL), pp. 772-778, 52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014, Baltimore, MD, United States, 6/22/14.
    Salloum W, Elfardy H, Alamir-Salloum L, Habash N, Diab M. Sentence level dialect identification for machine translation system selection. In Long Papers. Vol. 2. Association for Computational Linguistics (ACL). 2014. p. 772-778
    Salloum, Wael ; Elfardy, Heba ; Alamir-Salloum, Linda ; Habash, Nizar ; Diab, Mona. / Sentence level dialect identification for machine translation system selection. Long Papers. Vol. 2 Association for Computational Linguistics (ACL), 2014. pp. 772-778
    @inproceedings{d7e86c5695974768becb98bbd13be1a8,
    title = "Sentence level dialect identification for machine translation system selection",
    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.",
    author = "Wael Salloum and Heba Elfardy and Linda Alamir-Salloum and Nizar Habash and Mona Diab",
    year = "2014",
    month = "1",
    day = "1",
    language = "English (US)",
    isbn = "9781937284732",
    volume = "2",
    pages = "772--778",
    booktitle = "Long Papers",
    publisher = "Association for Computational Linguistics (ACL)",

    }

    TY - GEN

    T1 - Sentence level dialect identification for machine translation system selection

    AU - Salloum, Wael

    AU - Elfardy, Heba

    AU - Alamir-Salloum, Linda

    AU - Habash, Nizar

    AU - Diab, Mona

    PY - 2014/1/1

    Y1 - 2014/1/1

    N2 - 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.

    AB - 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.

    UR - http://www.scopus.com/inward/record.url?scp=84906932173&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=84906932173&partnerID=8YFLogxK

    M3 - Conference contribution

    SN - 9781937284732

    VL - 2

    SP - 772

    EP - 778

    BT - Long Papers

    PB - Association for Computational Linguistics (ACL)

    ER -