The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
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EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
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EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
Cost-sensitive active learning for computer-assisted translation
Pattern Recognition Letters
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In statistical machine translation (SMT), it is known that performance declines when the training data is in a different domain from the test data. Nevertheless, it is frequently necessary to supplement scarce in-domain training data with out-of-domain data. In this paper, we first try to relate the effect of the out-of-domain data on translation performance to measures of corpus similarity, then we separately analyse the effect of adding the out-of-domain data at different parts of the training pipeline (alignment, phrase extraction, and phrase scoring). Through experiments in 2 domains and 8 language pairs it is shown that the out-of-domain data improves coverage and translation of rare words, but may degrade the translation quality for more common words.