A systematic comparison of various statistical alignment models
Computational Linguistics
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Combining linguistic and machine learning techniques for word alignment improvement
Combining linguistic and machine learning techniques for word alignment improvement
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A correction model for word alignments
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
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Combining word alignments trained in two translation directions has mostly relied on heuristics that are not directly motivated by intended applications. We propose a novel method that performs combination as an optimization process. Our algorithm explicitly maximizes the effectiveness function with greedy search for phrase table training or synchronized grammar extraction. Experimental results show that the proposed method leads to significantly better translation quality than existing methods. Analysis suggests that this simple approach is able to maintain accuracy while maximizing coverage.