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Improving phrase-based statistical machine translation with morphosyntactic transformation
- 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.
- Subjects :
- Linguistics and Language
Phrase
Parsing
Grammar
Machine translation
business.industry
Computer science
media_common.quotation_subject
Speech recognition
Phrase structure rules
computer.software_genre
Language and Linguistics
Transformation (function)
Rule-based machine translation
Artificial Intelligence
Artificial intelligence
Computational linguistics
business
computer
Software
Natural language processing
media_common
Subjects
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