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Improving phrase-based statistical machine translation with morphosyntactic transformation

Authors :
Akira Shimazu
Thai Phuong Nguyen
Source :
Machine Translation. 20:147-166
Publication Year :
2007
Publisher :
Springer Science and Business Media LLC, 2007.

Abstract

We present a phrase-based statistical machine translation approach which uses linguistic analysis in the preprocessing phase. The linguistic analysis includes morphological transformation and syntactic transformation. Since the word-order problem is solved using syntactic transformation, there is no reordering in the decoding phase. For morphological transformation, we use hand-crafted transformational rules. For syntactic transformation, we propose a transformational model based on a probabilistic context-free grammar. This model is trained using a bilingual corpus and a broad-coverage parser of the source language. This approach is applicable to language pairs in which the target language is poor in resources. We considered translation from English to Vietnamese and from English to French. Our experiments showed significant BLEU-score improvements in comparison with Pharaoh, a state-of-the-art phrase-based SMT system.

Details

ISSN :
15730573 and 09226567
Volume :
20
Database :
OpenAIRE
Journal :
Machine Translation
Accession number :
edsair.doi...........eb548d5a00b46c6821d6d5d01ee09f78
Full Text :
https://doi.org/10.1007/s10590-007-9022-1