Stochastic inversion transduction grammars and bilingual parsing of parallel corpora
Computational Linguistics
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
BLEU: a method for automatic evaluation of machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
The Alignment Template Approach to Statistical Machine Translation
Computational Linguistics
The Proposition Bank: An Annotated Corpus of Semantic Roles
Computational Linguistics
Joint learning improves semantic role labeling
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Tree-to-string alignment template 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
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
Synchronous binarization for 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
Semantic role features for machine translation
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Rule Markov models for fast tree-to-string translation
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Utilizing target-side semantic role labels to assist hierarchical phrase-based machine translation
SSST-5 Proceedings of the Fifth Workshop on Syntax, Semantics and Structure in Statistical Translation
HPSG-Based Preprocessing for English-to-Japanese Translation
ACM Transactions on Asian Language Information Processing (TALIP)
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We propose three enhancements to the tree-to-string (TTS) transducer for machine translation: first-level expansion-based normalization for TTS templates, a syntactic alignment framework integrating the insertion of unaligned target words, and subtree-based n-gram model addressing the tree decomposition probability. Empirical results show that these methods improve the performance of a TTS transducer based on the standard BLEU-4 metric. We also experiment with semantic labels in a TTS transducer, and achieve improvement over our baseline system.