Making large-scale support vector machine learning practical
Advances in kernel methods
Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
Three heads are better than one
ANLC '94 Proceedings of the fourth conference on Applied natural language processing
Using language and translation models to select the best among outputs from multiple MT systems
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
A machine learning approach to the automatic evaluation of machine translation
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Optimization, maxent models, and conditional estimation without magic
NAACL-Tutorials '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: Tutorials - Volume 5
Multi-engine machine translation with voted language model
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Clause restructuring for statistical machine translation
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Grammatical machine translation
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
First steps towards multi-engine machine translation
ParaText '05 Proceedings of the ACL Workshop on Building and Using Parallel Texts
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Where's the verb?: correcting machine translation during question answering
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Empirical methods in natural language generation
User edits classification using document revision histories
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
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One style of Multi-Engine Machine Translation architecture involves choosing the best of a set of outputs from different systems. Choosing the best translation from an arbitrary set, even in the presence of human references, is a difficult problem; it may prove better to look at mechanisms for making such choices in more restricted contexts. In this paper we take a classification-based approach to choosing between candidates from syntactically informed translations. The idea is that using multiple parsers as part of a classifier could help detect syntactic problems in this context that lead to bad translations; these problems could be detected on either the source side---perhaps sentences with difficult or incorrect parses could lead to bad translations---or on the target side---perhaps the output quality could be measured in a more syntactically informed way, looking for syntactic abnormalities. We show that there is no evidence that the source side information is useful. However, a target-side classifier, when used to identify particularly bad translation candidates, can lead to significant improvements in BLEU score. Improvements are even greater when combined with existing language and alignment model approaches.