Research on semantic-based passive transformation in Chinese-English machine translation

Abstract

Passive voice is widely used in English while it is less used in Chinese, which is more prevalent in patent documents. The difference requires us to transform the voice in Chinese-English machine translation in order to make the result more smooth and natural. Previous studies in this field are based on statistics, but the effect is not very good. In this paper we propose a strategy to deal with the Chinese-English passive voice transformation from the perspective of semantic. Through analyzing the sentences, a series of transformation rules are summarized. Then we test them in our system. Experiment results show that the transformation rules can achieve an accuracy of 89.1% overall. © Springer-Verlag Berlin Heidelberg 2013.