A systematic comparison of various statistical alignment models
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
Minimum error rate training in statistical machine translation
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Moses: open source toolkit for statistical machine translation
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
Efficient handling of N-gram language models for statistical machine translation
StatMT '07 Proceedings of the Second Workshop on Statistical Machine Translation
An empirical study on development set selection strategy for machine translation learning
WMT '10 Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR
Automatic filtering of bilingual corpora for statistical machine translation
NLDB'05 Proceedings of the 10th international conference on Natural Language Processing and Information Systems
Instance selection for machine translation using feature decay algorithms
WMT '11 Proceedings of the Sixth Workshop on Statistical Machine Translation
UPM system for the translation task
WMT '11 Proceedings of the Sixth Workshop on Statistical Machine Translation
Findings of the 2012 workshop on statistical machine translation
WMT '12 Proceedings of the Seventh Workshop on Statistical Machine Translation
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This paper describes the UPM system for the Spanish-English translation task at the NAACL 2012 workshop on statistical machine translation. This system is based on Moses. We have used all available free corpora, cleaning and deleting some repetitions. In this paper, we also propose a technique for selecting the sentences for tuning the system. This technique is based on the similarity with the sentences to translate. With our approach, we improve the BLEU score from 28.37% to 28.57%. And as a result of the WMT12 challenge we have obtained a 31.80% BLEU with the 2012 test set. Finally, we explain different experiments that we have carried out after the competition.