A purely monotonic approach to machine translation for similar languages

Ye Kyaw Thu, Andrew Finch, Eiichiro Sumita, Yoshinori Sagisaka

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

    Abstract

    This paper investigates the effect of taking a strictly monotonic approach to machine translation for a restricted set of suitable language pairs. We studied the effect of decoding monotonically for a set of language pairs which has similar word order characteristics and found that for some language pairs - namely language pairs where both languages are in SOV order - there was almost no difference in machine translation quality. The results of this experiment motivated the extension of the monotonic approach into the alignment stage of the training. We used a Bayesian non-parametric aligner that has been shown to out-perform GIZA++ in combination with the grow-diag-final- and heuristic on transliteration data. Our results show that the monotonic aligner was able to match the performance of the GIZA++ baseline, and gains in translation performance were obtained by integrating both aligners into the systems.

    Original languageEnglish
    Title of host publicationProceedings - 2013 International Conference on Asian Language Processing, IALP 2013
    Pages107-110
    Number of pages4
    DOIs
    Publication statusPublished - 2013
    Event2013 International Conference on Asian Language Processing, IALP 2013 - Urumqi, Xinjiang
    Duration: 2013 Aug 172013 Aug 19

    Other

    Other2013 International Conference on Asian Language Processing, IALP 2013
    CityUrumqi, Xinjiang
    Period13/8/1713/8/19

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    Keywords

    • bilingual alignment
    • machine translation
    • monotonic decoding

    ASJC Scopus subject areas

    • Software

    Cite this

    Thu, Y. K., Finch, A., Sumita, E., & Sagisaka, Y. (2013). A purely monotonic approach to machine translation for similar languages. In Proceedings - 2013 International Conference on Asian Language Processing, IALP 2013 (pp. 107-110). [6646015] https://doi.org/10.1109/IALP.2013.31