The AMU-UEDIN Submission to the WMT16 News Translation Task: Attention-based NMT Models as Feature Functions in Phrase-based SMT

Marcin Junczys-Dowmunt, Tomasz Dwojak, Rico Sennrich

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

Abstract

This paper describes the AMU-UEDIN submissions to the WMT 2016 shared task on news translation. We explore methods of decode-time integration of attention-based neural translation models with phrase-based statistical machine translation. Efficient batch-algorithms for GPU-querying are proposed and implemented. For English-Russian, our system stays behind the state-of-the-art pure neural models in terms of BLEU. Among restricted systems, manual evaluation places it in the first cluster tied with the pure neural model. For the Russian-English task, our submission achieves the top BLEU result, outperforming the best pure neural system by 1.1 BLEU points and our own phrase-based baseline by 1.6 BLEU. After manual evaluation, this system is the best restricted system in its own cluster. In follow-up experiments we improve results by additional 0.8 BLEU.
Original languageEnglish
Title of host publicationProceedings of the First Conference on Machine Translation, Volume 2: Shared Task Papers
Place of PublicationBerlin, Germany
PublisherAssociation for Computational Linguistics
Pages319-325
Number of pages7
ISBN (Electronic)978-1-945626-10-4
DOIs
Publication statusPublished - 7 Aug 2016
EventFirst Conference on Machine Translation - Berlin, Germany
Duration: 11 Aug 201612 Aug 2016
http://www.statmt.org/wmt16/

Conference

ConferenceFirst Conference on Machine Translation
Abbreviated titleWMT16
CountryGermany
CityBerlin
Period11/08/1612/08/16
Internet address

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