Word order is one of the most significant differences between the Chinese and Vietnamese. In the phrase-based statistical machine translation, the reordering model will learn reordering rules from bilingual corpora. If the bilingual corpora are large and good enough, the reordering rules are exact and coverable. However, Chinese-Vietnamese is a low-resource language pair, the extraction of reordering rules is limited. This leads to the quality of reordering in Chinese-Vietnamese machine translation is not high. In this paper, we have combined Chinese dependency relation and Chinese-Vietnamese word alignment results in order to pre-order Chinese word order to be suitable to Vietnamese one. The experimental results show that our methodology has improved the machine translation performance compared to the translation system using only the reordering models of phrase-based statistical machine translation.

This study aims to identify grammatical relations (GRs) in Korean sentences. The key task is to find the GRs in sentences in terms of such GR categories as subject, object, and adverbial. To overcome this problem, we are faced with the structural ambiguity and the grammatical relational ambiguity. We propose a statistical model, which resolves the grammatical relational ambiguity first, and then resolves structural ambiguity by using the probabilities of the GRs given noun phrases and verb phrases in sentences. The proposed model uses the characteristics of the Korean language such as distance, no-crossing and case property. We showed that consideration of such characteristics produces better results than without consideration by experiments. We attempt to enhance our system by estimating the probabilities of the proposed model with the Maximum Entropy (ME) model, and with Support Vector Machines (SVM) classifiers and we confirm that SVM classifiers improved the performance of our proposed model through experiments. Through an experiment with a tree and GR tagged corpus for training the model, we achieved an overall accuracy of 84.8%, 94.1%, and 84.8% in identifying subject, object, and adverbial relations in sentences, respectively.