A bilingual word alignment algorithm of Naxi-Chinese based on feature constraint models

A bilingual word alignment algorithm of Naxi-Chinese based on feature constraint models is proposed to reduce the difficulty of bilingual word alignment for Naxi-Chinese which has huge difference in syntactic structure. Two feature constraint models-interval distortion model and position transformation model are established by counting the traits of interval distortion and position transformation in corpus, and are integrated into a log-linear framework of word alignment. Then parameters in the models are trained using the minimum error rate algorithm and the best alignment results are eventually searched. Experimental results on IBM Model3 show that the proposed algorithm increases the word alignment accuracy of Naxi-Chinese about 21.9%.