Abstract
Incorporating target syntax into phrase-based machine translation (PBMT) can generate syntactically well-formed translations. We propose a novel phrasal syntactic category sequence (PSCS) model which allows a PBMT decoder to prefer more grammatical translations. We parse all the sentences on the target side of the bilingual training corpus. In the standard phrase pair extraction procedure, we assign a syntactic category to each phrase pair and build a PSCS model from the parallel training data. Then, we log linearly incorporate the PSCS model into a standard PBMT system. Our method is very simple and yields a 0.7 BLEU point improvement when compared to the baseline PBMT system.
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© 2012 Springer-Verlag Berlin Heidelberg
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Cao, H., Sumita, E., Zhao, T., Li, S. (2012). Phrasal Syntactic Category Sequence Model for Phrase-Based MT. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2012. Lecture Notes in Computer Science, vol 7182. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28601-8_5
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DOI: https://doi.org/10.1007/978-3-642-28601-8_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28600-1
Online ISBN: 978-3-642-28601-8
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