The nature of statistical learning theory
The nature of statistical learning theory
A systematic comparison of various statistical alignment models
Computational Linguistics
Programming collective intelligence
Programming collective intelligence
Moses: open source toolkit for statistical machine translation
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
Joshua: an open source toolkit for parsing-based machine translation
StatMT '09 Proceedings of the Fourth Workshop on Statistical Machine Translation
Fluency, adequacy, or HTER?: exploring different human judgments with a tunable MT metric
StatMT '09 Proceedings of the Fourth Workshop on Statistical Machine Translation
Further experiments with shallow hybrid MT systems
WMT '10 Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR
Personal translator at WMT2011: a rule-based MTsystem with hybrid components
WMT '11 Proceedings of the Sixth Workshop on Statistical Machine Translation
Stochastic parse tree selection for an existing RBMT system
WMT '11 Proceedings of the Sixth Workshop on Statistical Machine Translation
Findings of the 2012 workshop on statistical machine translation
WMT '12 Proceedings of the Seventh Workshop on Statistical Machine Translation
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We describe a substitution-based system for hybrid machine translation (MT) that has been extended with machine learning components controlling its phrase selection. The approach is based on a rule-based MT (RBMT) system which creates template translations. Based on the rule-based generation parse tree and target-to-target alignments, we identify the set of "interesting" translation candidates from one or more translation engines which could be substituted into our translation templates. The substitution process is either controlled by the output from a binary classifier trained on feature vectors from the different MT engines, or it is depending on weights for the decision factors, which have been tuned using MERT. We are able to observe improvements in terms of BLEU scores over a baseline version of the hybrid system.