Identifying word correspondence in parallel texts
HLT '91 Proceedings of the workshop on Speech and Natural Language
A systematic comparison of various statistical alignment models
Computational Linguistics
Models of translational equivalence among words
Computational Linguistics
Accurate methods for the statistics of surprise and coincidence
Computational Linguistics - Special issue on using large corpora: I
The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
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
A probability model to improve word alignment
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
A discriminative framework for bilingual word alignment
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Beyond SumBasic: Task-focused summarization with sentence simplification and lexical expansion
Information Processing and Management: an International Journal
Statistical machine translation
ACM Computing Surveys (CSUR)
A word alignment model based on multiobjective evolutionary algorithms
Computers & Mathematics with Applications
Improving phrase-based statistical translation through combination of word alignments
FinTAL'06 Proceedings of the 5th international conference on Advances in Natural Language Processing
A chunk-driven bootstrapping approach to extracting translation patterns
CICLing'10 Proceedings of the 11th international conference on Computational Linguistics and Intelligent Text Processing
Generalizing sampling-based multilingual alignment
Machine Translation
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Bilingual word alignment forms the foundation of current work on statistical machine translation. Standard word-alignment methods involve the use of probabilistic generative models that are complex to implement and slow to train. In this paper we show that it is possible to approach the alignment accuracy of the standard models using algorithms that are much faster, and in some ways simpler, based on basic word-association statistics.