Study on Phrase Processing of English Texts by Neural Machine Translation
TL;DR
A statistical machine translation (SMT)-based phrase processing method that adds phrases in different suffix forms to the source-end sentences was proposed to improve translation quality and the results verify that the proposed method is reliable and can be applied in practice.
Abstract
The current research on neural machine translation (NMT) rarely involves phrase processing, which leads to poor translation quality. This paper first gives a brief introduction to NMT and the Transformer model. Then, a statistical machine translation (SMT)-based phrase processing method that adds phrases in different suffix forms to the source-end sentences was proposed to improve translation quality. Experiments were conducted on the China Workshop on Machine Translation 2018 (CWMT2018) dataset (Chinese-English) and the WMT2014 dataset (English-German). The results showed that, among the three suffix forms, only adding the target phrase sequence in the suffix form was conducive to improving the translation quality of the Transformer model: the mean bilingual evaluation understudy (BLEU) value increased by 0.0254 on the Chinese-English dataset and by 0.0105 on the English-German dataset compared with the baseline model. Compared with NMT models such as seq2seq, the Transformer model combined with phrase processing obtained the best BLEU value, and the resulting translation was more in line with the reference translation. The results verify that the proposed method is reliable and can be applied in practice.