Machine Learning - Special issue on inductive transfer
Evaluating translational correspondence using annotation projection
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
A probability model to improve word alignment
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Log-linear models for word alignment
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Discriminative word alignment with conditional random fields
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Semi-supervised training for statistical word alignment
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
A discriminative framework for bilingual word alignment
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Soft syntactic constraints for word alignment through discriminative training
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Two languages are better than one (for syntactic parsing)
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
MLR '04 Proceedings of the Workshop on Multilingual Linguistic Ressources
IEEE Transactions on Knowledge and Data Engineering
Discriminative pruning for discriminative ITG alignment
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
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Word alignment and parsing are two important components for syntax based machine translation. The inconsistent models for alignment and parsing caused problems during translation pair extraction. In this paper, we do word alignment and dependency parsing in a multi-task learning framework, in which word alignment and dependency parsing are consistent and assisted with each other. Our experiments show significant improvement not only for both word alignment and dependency parsing, but also the final translation performance.