The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
An efficient method for determining bilingual word classes
EACL '99 Proceedings of the ninth conference on European chapter of the 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
Clause restructuring for statistical machine translation
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
N-gram-based Machine Translation
Computational Linguistics
Statistical machine reordering
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Ngram-based statistical machine translation enhanced with multiple weighted reordering hypotheses
StatMT '07 Proceedings of the Second Workshop on Statistical Machine Translation
Novel reordering approaches in phrase-based statistical machine translation
ParaText '05 Proceedings of the ACL Workshop on Building and Using Parallel Texts
An Ngram-based reordering model
Computer Speech and Language
(Meta-) evaluation of machine translation
StatMT '07 Proceedings of the Second Workshop on Statistical Machine Translation
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One main challenge of statistical machine translation (SMT) is dealing with word order. The main idea of the statistical machine reordering (SMR) approach is to use the powerful techniques of SMT systems to generate a weighted reordering graph for SMT systems. This technique supplies reordering constraints to an SMT system, using statistical criteria. In this paper, we experiment with different graph pruning which guarantees the translation quality improvement due to reordering at a very low increase of computational cost. The SMR approach is capable of generalizing reorderings, which have been learned during training, by using word classes instead of words themselves. We experiment with statistical and morphological classes in order to choose those which capture the most probable reorderings. Satisfactory results are reported in the WMT07 Es/En task. Our system outperforms in terms of BLEU the WMT07 Official baseline system.