Parser Self-Training for Syntax-Based Machine Translation

Abstract

In syntax-based machine translation, it is known that the accuracy of parsing greatly affects the translation accuracy. Self-training, which uses parser output as training data, is one method to improve the parser accuracy. However, because automatically generated parse trees often include errors, these parse trees do not always contribute to improving accuracy. In this paper, we propose a method for removing noisy incorrect parse trees from the training data to improve the effect of self-training by using automatic evaluation metrics of translations. Specifically, we perform syntax-based machine translation using n-best parse trees, then we re-scoring parse trees based on the automatic evaluation score of translations. By using the parse trees that have higher score among the candidates for self-training, we can improve parsing and machine translation accuracy by using parallel corpora that are not annotated syntax structure. In experiments, using higher score parse trees for self-training, we found that our self-trained parsers significantly improve a state-of-the-art syntax-based machine translation system in two language pairs, and self-trained parsers significantly improve the accuracy of the parsing itself.