A Chinese-Naxi Tree-to-Tree Machine Translation Method Based on Subtree Alignment

Abstract

In allusion to the syntactic differences between Chinese and Naxi language, the thesis presents a tree-to-tree method of Chinese-Naxi machine translation based on subtree alignment. In this method, we define a subtree alignment model, providing its inference probability, and solve the alignment missing problem of Chinese-Naxi alignment by updating nodes (insert or delete). And then we train the subtree alignment model by EM algorithm, merging subtree alignment to the translation model. Finally we extract the template of Chinese-Naxi translation, adopting the extraction algorithm based on matrix, and implement the Chinese-Naxi machine translation. Result of the contrast experiment shows that, compared to the Chinese-Naxi machine translation method based on tree-to-tree translation, the translation accuracy increased after importing subtree alignment.