Abstract
The state of the art statistical machine translation (SMT) systems are based on phrase (a group of words), which are modeled using log-linear maximum entropy framework. In this paper, we constructed a phrase-based statistical machine translation system with additional feature models. The translation model is combined with four specific additional feature functions. When comparing our system with the baseline system of IWSLT2005, we can conclude that our system improve the SMT system accuracy with the same corpus.
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© 2006 Springer-Verlag Berlin Heidelberg
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Li, M., Gao, P., Zhang, J., Luo, Y. (2006). Phrase-Based Statistical Machine Translation by Using Reordering Search and Additional Features. In: Huang, DS., Li, K., Irwin, G.W. (eds) Computational Intelligence. ICIC 2006. Lecture Notes in Computer Science(), vol 4114. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-37275-2_62
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DOI: https://doi.org/10.1007/978-3-540-37275-2_62
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Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37274-5
Online ISBN: 978-3-540-37275-2
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