Improving Word Alignment for Statistical Machine Translation Based on Constraints

Word alignment is an important and fundamental task for building a statistical machine translation (SMT) system. However, obtaining word-level alignments in parallel corpora with high accuracy is still a challenge. In this paper, we propose a new method, which is based on constraint approach, to improve the quality of word alignment. Our experiments show that using constraints for the parameter estimation of the IBM models reduces the alignment error rate down to 7.26% and increases the BLEU score to 5%, in the case of translation from English to Vietnamese.