A maximum entropy approach to natural language processing
Computational Linguistics
A systematic comparison of various statistical alignment models
Computational Linguistics
Maximum entropy models for natural language ambiguity resolution
Maximum entropy models for natural language ambiguity resolution
The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Discriminative training and maximum entropy models for statistical machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for 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
An evaluation exercise for word alignment
HLT-NAACL-PARALLEL '03 Proceedings of the HLT-NAACL 2003 Workshop on Building and using parallel texts: data driven machine translation and beyond - Volume 3
Maximum entropy models for FrameNet classification
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Log-linear models for word alignment
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
A localized prediction model for statistical machine translation
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
NeurAlign: combining word alignments using neural networks
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
A discriminative matching approach to word alignment
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
A discriminative framework for bilingual word alignment
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
A maximum entropy word aligner for Arabic-English machine translation
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Improved HMM alignment models for languages with scarce resources
ParaText '05 Proceedings of the ACL Workshop on Building and Using Parallel Texts
Statistical machine translation
ACM Computing Surveys (CSUR)
Tera-scale translation models via pattern matching
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Confidence measure for word alignment
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 2 - Volume 2
Diversify and combine: improving word alignment for machine translation on low-resource languages
ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
Improving word alignment by semi-supervised ensemble
CoNLL '10 Proceedings of the Fourteenth Conference on Computational Natural Language Learning
Combining unsupervised and supervised alignments for MT: an empirical study
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Discriminative word alignment by linear modeling
Computational Linguistics
A maximum entropy approach to syntactic translation rule filtering
CICLing'10 Proceedings of the 11th international conference on Computational Linguistics and Intelligent Text Processing
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This paper presents a new approach to combining outputs of existing word alignment systems. Each alignment link is represented with a set of feature functions extracted from linguistic features and input alignments. These features are used as the basis of alignment decisions made by a maximum entropy approach. The learning method has been evaluated on three language pairs, yielding significant improvements over input alignments and three heuristic combination methods. The impact of word alignment on MT quality is investigated, using a phrase-based MT system.