Perplexity Minimization for Translation Model Domain Adaptation in Statistical Machine Translation

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

Abstract

We investigate the problem of domain adaptation for parallel data in Statistical Machine Translation (SMT). While techniques for domain adaptation of monolingual data can be borrowed for parallel data, we explore conceptual differences between translation model and language model domain adaptation and their effect on performance, such as the fact that translation models typically consist of several features that have different characteristics and can be optimized separately. We also explore adapting multiple (4-10) data sets with no a priori distinction between in-domain and out-of-domain data except for an in-domain development set.
Original languageEnglish
Title of host publicationProceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
Place of PublicationStroudsburg, PA, USA
PublisherAssociation for Computational Linguistics
Pages539-549
Number of pages11
ISBN (Print)978-1-937284-19-0
Publication statusPublished - 2012

Publication series

NameEACL '12
PublisherAssociation for Computational Linguistics

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