Skip to content
University of Maryland LibrariesDigital Repository at the University of Maryland
    • Login
    View Item 
    •   DRUM
    • Center for Advanced Study of Language
    • Center for Advanced Study of Language Research Works
    • View Item
    •   DRUM
    • Center for Advanced Study of Language
    • Center for Advanced Study of Language Research Works
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Bucking the Trend: Large-Scale Cost-Focused Active Learning for Statistical Machine Translation

    Thumbnail
    View/Open
    buckingTheTrendActiveLearningMachineTranslationACL2010.pdf (509.9Kb)
    No. of downloads: 181

    Date
    2010-07
    Author
    Bloodgood, Michael
    Callison-Burch, Chris
    Citation
    Michael Bloodgood and Chris Callison-Burch. 2010. Bucking the trend: cost-focused active learning for statistical machine translation. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 854-864, Uppsala, Sweden, July. Association for Computational Linguistics.
    Metadata
    Show full item record
    Abstract
    We explore how to improve machine translation systems by adding more translation data in situations where we already have substantial resources. The main challenge is how to buck the trend of diminishing returns that is commonly encountered. We present an active learning-style data solicitation algorithm to meet this challenge. We test it, gathering annotations via Amazon Mechanical Turk, and find that we get an order of magnitude increase in performance rates of improvement.
    URI
    http://hdl.handle.net/1903/15549
    Collections
    • Center for Advanced Study of Language Research Works

    Related items

    Showing items related by title, author, creator and subject.

    • Improved Online Learning and Modeling for Feature-Rich Discriminative Machine Translation 

      Eidelman, Vladimir (2013)
      Most modern statistical machine translation (SMT) systems learn how to translate by constructing a discriminative model based on statistics from the data. A growing number of methods for discriminative training have been ...
    • Searching to Translate and Translating to Search: When Information Retrieval Meets Machine Translation 

      Ture, Ferhan (2013)
      With the adoption of web services in daily life, people have access to tremendous amounts of information, beyond any human's reading and comprehension capabilities. As a result, search technologies have become a fundamental ...
    • Handling Translation Divergences in Generation-Heavy Hybrid Machine Translation 

      Habash, Nizar; Dorr, Bonnie (2002-04-04)
      This paper describes a novel approach for handling translation divergences in a Generation-Heavy Hybrid Machine Translation (GHMT) system. The approach depends on the existence of rich target language resources such as ...

    DRUM is brought to you by the University of Maryland Libraries
    University of Maryland, College Park, MD 20742-7011 (301)314-1328.
    Please send us your comments.
    Web Accessibility
     

     

    Browse

    All of DRUMCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

    My Account

    LoginRegister
    Pages
    About DRUMAbout Download Statistics

    DRUM is brought to you by the University of Maryland Libraries
    University of Maryland, College Park, MD 20742-7011 (301)314-1328.
    Please send us your comments.
    Web Accessibility