Analysis of Different Tense Recognition and Translation for Chinese–English Translation using Machine Translation

Abstract

In machine translation, the processing of tenses is a crucial element. This paper briefly introduces neural machine translation (NMT) and analyzes the NMT model based on long shortterm memory (LSTM) and bidirectional-LSTM (Bi-LSTM). A neural network method based on LSTM was designed to recognize verb tenses in Chinese by collating bilingual corpora. The method was combined with NMT to recognize and translate different tenses. Experiments were conducted on the dataset. The results suggested that Bi-LSTM had a higher accuracy in tense recognition than LSTM, with an average accuracy of 89.89%, 7.8% higher than LSTM. A BRcomparison of the baseline showed that the NMT model based on Bi-LSTM had the highest bilingual evaluation understudy (BLEU) score. The BLEU score of Bi-LSTM combined with tense recognition was improved by 6.9. The experimental results demonstrated the effectiveness of Bi-LSTM combined with tense recognition in recognizing and translating different tenses in Chinese–English translations. This method can be applied in practice.