The University of Maryland statistical machine translation system for the Fifth Workshop on Machine Translation

Authors:
Vladimir Eidelman;Chris Dyer;Philip Resnik
Affiliations:
UMIACS Laboratory for Computational Linguistics and Information Processing;UMIACS Laboratory for Computational Linguistics and Information Processing and University of Maryland, College Park;UMIACS Laboratory for Computational Linguistics and Information Processing and University of Maryland, College Park
Venue:
WMT '10 Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR
Year:
2010

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Abstract

This paper describes the system we developed to improve German-English translation of News text for the shared task of the Fifth Workshop on Statistical Machine Translation. Working within cdec, an open source modular framework for machine translation, we explore the benefits of several modifications to our hierarchical phrase-based model, including segmentation lattices, minimum Bayes Risk decoding, grammar extraction methods, and varying language models. Furthermore, we analyze decoder speed and memory performance across our set of models and show there is an important trade-off that needs to be made.