The Edinburgh/JHU Phrase-based Machine Translation Systems for WMT 2015

Barry Haddow, Matthias Huck, Alexandra Birch, Nikolay Bogoychev, Philipp Koehn

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

Abstract

This paper describes the submission of the University of Edinburgh and the Johns Hopkins University for the shared translation task of the EMNLP 2015 Tenth Workshop on Statistical Machine Translation (WMT 2015). We set up phrase-based statistical machine translation systems for all ten language pairs of this year’s evaluation campaign, which are English paired with Czech, Finnish, French, German, and Russian in both translation directions.

Novel research directions we investigated include: neural network language models and bilingual neural network language models, a comprehensive use of word classes, and sparse lexicalized reordering features.
Original languageEnglish
Title of host publicationProceedings of the Tenth Workshop on Statistical Machine Translation
Place of PublicationLisbon, Portugal
PublisherAssociation for Computational Linguistics
Pages126-133
Number of pages8
Publication statusPublished - 1 Sep 2015

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