A Neural Network Classifier Based on Dependency Tree for English-Vietnamese Statistical Machine Translation

Abstract

Reordering in MT is a major challenge when translating between languages with different of sentence structures. In Phrase-based statistical machine translation (PBSMT) systems, syntactic pre-ordering is a commonly used pre-processing technique. This technique can be used to adjust the syntax of the source language to that of the target language by changing the word order of a source sentence prior to translation and solving to overcome a weakness of classical phrase-based translation systems: long distance reordering. In this paper, we propose a new pre-ordering approach by defining dependency-based features and using a neural network classifier for reordering the words in the source sentence into the same order in target sentence. Experiments on English-Vietnamese machine translation showed that our approach yielded a statistically significant improvement compared to our prior baseline phrase-based SMT system.