We present a phrase-based SMT approach in which the wordorder problem is solved using syntactic transformation in the preprocessing phase (There is no reordering in the decoding phase.) We describe a syntactic transformation model based on the probabilistic context-free grammar. This model is trained by using bilingual corpus and a broad coverage parser of the source language. This phrase-based SMT 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.
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