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 phrase-based, joint probability model for statistical machine translation
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Scaling phrase-based statistical machine translation to larger corpora and longer phrases
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
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In this paper, we address the topic of how to estimate phrase-based models from very large corpora and apply them in statistical machine translation. The great number of sentence pairs contained in recent corpora like the well-known Europarlcorpus have enormously increased the memory requirements to train phrase-based models and to apply them within a decoding process. We propose a general framework that deals with this problem without introducing significant time overhead by means of the combination of different scaling techniques. This new framework is based on the use of counts instead of probabilities, and on the concept of cache memory.