A maximum entropy approach to natural language processing
Computational Linguistics
Maximum Entropy Markov Models for Information Extraction and Segmentation
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Stochastic inversion transduction grammars and bilingual parsing of parallel corpora
Computational Linguistics
Decoding complexity in word-replacement translation models
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
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
Dividing and conquering long sentences in a translation system
HLT '91 Proceedings of the workshop on Speech and Natural Language
A hierarchical phrase-based model for statistical machine translation
ACL '05 Proceedings of the 43rd 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
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
Reordering constraints for phrase-based statistical machine translation
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Classifying chart cells for quadratic complexity context-free inference
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Extracting synchronous grammar rules from word-level alignments in linear time
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
A syntax-driven bracketing model for phrase-based translation
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
Parsing the penn chinese treebank with semantic knowledge
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Learning phrase boundaries for hierarchical phrase-based translation
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Learning to transform and select elementary trees for improved syntax-based machine translations
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
A statistical tree annotator and its applications
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Reordering constraint based on document-level context
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
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Constrained decoding is of great importance not only for speed but also for translation quality. Previous efforts explore soft syntactic constraints which are based on constituent boundaries deduced from parse trees of the source language. We present a new framework to establish soft constraints based on a more natural alternative: translation boundary rather than constituent boundary. We propose simple classifiers to learn translation boundaries for any source sentences. The classifiers are trained directly on word-aligned corpus without using any additional resources. We report the accuracy of our translation boundary classifiers. We show that using constraints based on translation boundaries predicted by our classifiers achieves significant improvements over the baseline on large-scale Chinese-to-English translation experiments. The new constraints also significantly outperform constituent boundary based syntactic constrains.