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An Ngram-based reordering model
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DOI：10.1016/j.csl.2008.12.002.png)
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Abstract
En 中文
This paper describes in detail a novel approach to the reordering challenge in statistical machine translation (SMT). This Ngram-based reordering (NbR) approach uses the powerful techniques of SMT systems to generate a weighted reordering graph. Thus, statistical criteria reordering constraints are supplied to an SMT system, and this allows an extension to the SMT decoding search. The NbR approach is capable of generalizing reorderings that have been learned during training, through the use of word classes instead of words themselves. Improvement in translation performance is demonstrated with the EPPS task (Spanish and German to English) and the BTEC task (Arabic to English). (C) 2008 Elsevier Ltd. All rights reserved.
Keywords:
Statistical machine translation
Language modeling
Word reordering
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