A systematic comparison of various statistical alignment models
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Log-linear models for word alignment
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Discriminative word alignment with conditional random fields
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Learning accurate, compact, and interpretable tree annotation
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Improved discriminative bilingual word alignment
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Scalable inference and training of context-rich syntactic translation models
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A discriminative matching approach to word alignment
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A discriminative framework for bilingual word alignment
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A maximum entropy word aligner for Arabic-English machine translation
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Word alignment via quadratic assignment
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Hierarchical Phrase-Based Translation
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Soft syntactic constraints for word alignment through discriminative training
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Using syntax to improve word alignment precision for syntax-based machine translation
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Better word alignments with supervised ITG models
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Feature-rich language-independent syntax-based alignment for statistical machine translation
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We present a simple yet powerful hierarchical search algorithm for automatic word alignment. Our algorithm induces a forest of alignments from which we can efficiently extract a ranked k-best list. We score a given alignment within the forest with a flexible, linear discriminative model incorporating hundreds of features, and trained on a relatively small amount of annotated data. We report results on Arabic-English word alignment and translation tasks. Our model outperforms a GIZA++ Model-4 baseline by 6.3 points in F-measure, yielding a 1.1 Bleu score increase over a state-of-the-art syntax-based machine translation system.