Integrated linguistic to Statistical Machine Translation

• Main Author: Vương, Hoài Thu
• Format: Theses and Dissertations
• Language: other
• Published: Đại học Quốc gia Hà Nội 2016
• Subjects: Khoa học máy tính; Xử lý ngôn ngữ tự nhiên; Thông tin ngôn ngữ; Dịch máy
• Online Access: http://repository.vnu.edu.vn/handle/VNU_123/8256
• Institution: Vietnam National University, Hanoi

In the field of Natural Language Processing, automatic machine translation is an attractive application for a supporting user to translate some sentences in a language to others. Today, Phrase-based Statistical Machine Translation is the-state-of-the-art with benet in the word choosing, distortion based on the distance between words. However, we still have some problem with global dis-tortion model of different languages (long distance between words). In some previous studies, the linguistic information such as a syntax tree, morphology information or hierarchical of phrase is used. Similarly, we also use the syntax tree to help the distortion model. However, instead of using full parse tree, we use a shallow syntax tree (the height of tree is limited). By using some trans-formation rules, we can arrange the order of some nodes in the shallow syntax tree. Hence, we reorder the words in the sentence. A special point in our study is applying the transformation rule on the sentence in the source language to get new sentence with new order of words, which is similar with the target language, as preprocessing step before training translation model or decoding with beam search and log linear model. The experiment results from an English-Vietnamese pair showed that our approach achieves significant improvements over MOSES which is the state-of-the-art phrase based system