Integrating Pronunciation into Chinese-Vietnamese Statistical Machine Translation

摘要

Statistical machine translation for low-resource language suffers from the lack of abundant training corpora.Several methods,such as the use of a pivot language,have been proposed as a bridge to translate from one language to another.However,errors will accumulate during the extensive translation pipelines.In this paper,we propose an approach to low-resource language translation by exploiting the pronunciation correlations between languages.We find that the pronunciation features can improve both Chinese-Vietnamese and Vietnamese-Chinese translation qualities.Experimental results show that our proposed model yields effective improvements,and the translation performance (bilingual evaluation understudy score) is improved by a maximum value of 1.03.