A Multi-Domain Translation Model Framework for Statistical Machine Translation

Rico Sennrich, Holger Schwenk, Walid Aransa

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

Abstract / Description of output

While domain adaptation techniques for SMT have proven to be effective at improving translation quality, their practicality for a multi-domain environment is often limited because of the computational and human costs of developing and maintaining multiple systems adapted to different domains. We present an architecture that delays the computation of translation model features until decoding, allowing for the application of mixture-modeling techniques at decoding time. We also describe a method for unsupervised adaptation with development and test data from multiple domains. Experimental results on two language pairs demonstrate the effectiveness of both our translation model architecture and automatic clustering, with gains of up to 1 BLEU over unadapted systems and single-domain adaptation.
Original languageEnglish
Title of host publicationProceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Place of PublicationSofia, Bulgaria
PublisherAssociation for Computational Linguistics
Pages832-840
Number of pages9
Publication statusPublished - 1 Aug 2013
Event51st Annual Meeting of the Association for Computational Linguistics, ACL 2013 - Sofia, Bulgaria
Duration: 4 Aug 20139 Aug 2013

Conference

Conference51st Annual Meeting of the Association for Computational Linguistics, ACL 2013
Country/TerritoryBulgaria
CitySofia
Period4/08/139/08/13

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