BLEU: a method for automatic evaluation of machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Statistical phrase-based translation
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Minimum error rate training in statistical machine translation
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Improved statistical alignment models
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Phrasal cohesion and statistical machine translation
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
A projection extension algorithm for statistical machine translation
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Scalable inference and training of context-rich syntactic translation models
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Hierarchical Phrase-Based Translation
Computational Linguistics
Wide-coverage efficient statistical parsing with ccg and log-linear models
Computational Linguistics
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
A systematic comparison of phrase-based, hierarchical and syntax-augmented statistical MT
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Better binarization for the CKY parsing
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Efficient parsing for transducer grammars
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
CCG supertags in factored statistical machine translation
StatMT '07 Proceedings of the Second Workshop on Statistical Machine Translation
Syntax augmented machine translation via chart parsing
StatMT '06 Proceedings of the Workshop on Statistical Machine Translation
Better synchronous binarization for machine translation
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Accurate context-free parsing with combinatory categorial grammar
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
A word-class approach to labeling PSCFG rules for machine translation
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Learning hierarchical translation structure with linguistic annotations
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
A general-purpose rule extractor for SCFG-based machine translation
SSST-5 Proceedings of the Fifth Workshop on Syntax, Semantics and Structure in Statistical Translation
Joshua 3.0: syntax-based machine translation with the Thrax grammar extractor
WMT '11 Proceedings of the Sixth Workshop on Statistical Machine Translation
Hierarchical phrase-based translation representations
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
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Adding syntactic labels to synchronous context-free translation rules can improve performance, but labeling with phrase structure constituents, as in GHKM (Galley et al., 2004), excludes potentially useful translation rules. SAMT (Zollmann and Venugopal, 2006) introduces heuristics to create new non-constituent labels, but these heuristics introduce many complex labels and tend to add rarely-applicable rules to the translation grammar. We introduce a labeling scheme based on categorial grammar, which allows syntactic labeling of many rules with a minimal, well-motivated label set. We show that our labeling scheme performs comparably to SAMT on an Urdu--English translation task, yet the label set is an order of magnitude smaller, and translation is twice as fast.