Incorporating structural alignment biases into an attentional neural translation model

Trevor Cohn, Cong Duy Vu Hoang, Ekaterina Vymolova, Kaisheng Yao, Chris Dyer, Gholamreza Haffari

    Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

    58 Citations (Scopus)


    Neural encoder-decoder models of machine translation have achieved impressive results, rivalling traditional translation models. However their modelling formulation is overly simplistic, and omits several key inductive biases built into traditional models. In this paper we extend the attentional neural translation model to include structural biases from word based alignment models, including positional bias, Markov conditioning, fertility and agreement over translation directions. We show improvements over a baseline attentional model and standard phrase-based model over several language pairs, evaluating on difficult languages in a low resource setting.

    Original languageEnglish
    Title of host publicationThe 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL HLT 2016)
    Subtitle of host publicationProceedings of the Conference, June 12-17, 2016, San Diego, California, USA
    EditorsAni Nenkova, Owen Rambow
    Place of PublicationStroudsburg, PA
    PublisherAssociation for Computational Linguistics (ACL)
    Number of pages10
    ISBN (Print)9781941643914
    Publication statusPublished - 2016
    EventNorth American Association for Computational Linguistics 2016: Human Language Technologies - Sheraton San Diego Hotel & Marina, San Diego, United States of America
    Duration: 12 Jun 201617 Jun 2016
    Conference number: 15th


    ConferenceNorth American Association for Computational Linguistics 2016
    Abbreviated titleNAACL HLT 2016
    CountryUnited States of America
    CitySan Diego
    Internet address

    Cite this