Abstract
In statistical machine translation, many of the top-performing systems are phrase-based systems. This paper describes a phrase-based translation system and some improvements. We use more information to compute translation probability. The scaling factors of the log-linear models are estimated by the minimum error rate training that uses an evaluation criteria to balance BLEU and NIST scores. We extract phrase-template from initial phrases to deal with data sparseness and distortion problem through decoding. By re-ranking the n-best list of translations generated firstly, the system gets the final output. Some experiments concerned show that all these refinements are beneficial to get better results.
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© 2006 Springer-Verlag Berlin Heidelberg
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Yang, Z., Pang, W., Du, J., Wei, W., Xu, B. (2006). Some Improvements in Phrase-Based Statistical Machine Translation. In: Huo, Q., Ma, B., Chng, ES., Li, H. (eds) Chinese Spoken Language Processing. ISCSLP 2006. Lecture Notes in Computer Science(), vol 4274. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11939993_71
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DOI: https://doi.org/10.1007/11939993_71
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-49665-6
Online ISBN: 978-3-540-49666-3
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