A systematic comparison of various statistical alignment models
Computational Linguistics
The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
Stochastic inversion transduction grammars and bilingual parsing of parallel corpora
Computational Linguistics
Efficient normal-form parsing for combinatory categorial grammar
ACL '96 Proceedings of the 34th 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
Stochastic lexicalized inversion transduction grammar for alignment
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Synchronous binarization for machine translation
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Online Passive-Aggressive Algorithms
The Journal of Machine Learning Research
Inversion transduction grammar for joint phrasal translation modeling
SSST '07 Proceedings of the NAACL-HLT 2007/AMTA Workshop on Syntax and Structure in Statistical Translation
Better word alignments with supervised ITG models
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
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 has an exponentially large search space, which often makes exact inference infeasible. Recent studies have shown that inversion transduction grammars are reasonable constraints for word alignment, and that the constrained space could be efficiently searched using synchronous parsing algorithms. However, spurious ambiguity may occur in synchronous parsing and cause problems in both search efficiency and accuracy. In this paper, we conduct a detailed study of the causes of spurious ambiguity and how it effects parsing and discriminative learning. We also propose a variant of the grammar which eliminates those ambiguities. Our grammar shows advantages over previous grammars in both synthetic and real-world experiments.