A maximum entropy approach to natural language processing
Computational Linguistics
Stochastic inversion transduction grammars and bilingual parsing of parallel corpora
Computational Linguistics
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
Learning accurate, compact, and interpretable tree annotation
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Maximum entropy based phrase reordering model for statistical machine translation
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
Labeling chinese predicates with semantic roles
Computational Linguistics
Semantic roles for SMT: a hybrid two-pass model
NAACL-Short '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers
SSST '07 Proceedings of the NAACL-HLT 2007/AMTA Workshop on Syntax and Structure in Statistical Translation
Extending statistical machine translation with discriminative and trigger-based lexicon models
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Joint syntactic and semantic parsing of Chinese
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Semantic role features for machine translation
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Shallow semantic trees for SMT
WMT '11 Proceedings of the Sixth Workshop on Statistical Machine Translation
A Maximum-Entropy Segmentation Model for Statistical Machine Translation
IEEE Transactions on Audio, Speech, and Language Processing
Distortion Model Based on Word Sequence Labeling for Statistical Machine Translation
ACM Transactions on Asian Language Information Processing (TALIP)
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Predicate-argument structure contains rich semantic information of which statistical machine translation hasn't taken full advantage. In this paper, we propose two discriminative, feature-based models to exploit predicate-argument structures for statistical machine translation: 1) a predicate translation model and 2) an argument reordering model. The predicate translation model explores lexical and semantic contexts surrounding a verbal predicate to select desirable translations for the predicate. The argument reordering model automatically predicts the moving direction of an argument relative to its predicate after translation using semantic features. The two models are integrated into a state-of-the-art phrase-based machine translation system and evaluated on Chinese-to-English translation tasks with large-scale training data. Experimental results demonstrate that the two models significantly improve translation accuracy.