Effect of Named Entity Recognition on English-Vietnamese Neural Machine Translation

Abstract

Translators are becoming more and more popular and achieving reliable results since deep learning was born. English-Vietnamese machines translation (MT) still have limitations due to Vietnamese contain words with many different meanings, thus resulting in the lower accuracy of automatic MT systems. Our study applied Named Entity Recognition (NER) tool for Vietnamese sentences to determine the category of words in the English-Vietnamese parallel corpus with over 900K sentence pairs. Then, we performed experiments to assess the effect of NER on English-Vietnamese MT systems. The results showed that NER had a positive effect on MT with averagely 1.24 Bi-Lingual Evaluation Understudy (BLEU) scores and averagely 1.8 Translation Error Rate (TER) scores increased comparing to data without using NER.