The nature of statistical learning theory
The nature of statistical learning theory
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
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
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We describe a substitution-based, hybrid machine translation (MT) system that has been extended with a machine learning component controlling its phrase selection. Our approach is based on a rule-based MT (RBMT) system which creates template translations. Based on the generation parse tree of the RBMT system and standard word alignment computation, we identify potential "translation snippets" from one or more translation engines which could be substituted into our translation templates. The substitution process is controlled by a binary classifier trained on feature vectors from the different MT engines. Using a set of manually annotated training data, we are able to observe improvements in terms of BLEU scores over a baseline version of the hybrid system.