A Syntactic Transformation Model for Statistical Machine Translation
Thai Phuong Nguyen, Akira Shimazu, Le-Minh Nguyen, Vinh Van Nguyen · International Journal of Computer Processing Of Languages · 2007
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. Then we present two methods to solve the word-order problem using the transformational model. The first method deals with this problem in the preprocessing phase. There is no reordering in the decoding phase. The second method employs the syntactic transformation model in the decoding phase for phrase reordering within chunks. Speed is an advantage of this method. 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.