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

被引：0

作者：

Ni X. ^{[1
]}

机构：

[1] Department of International Educational Exchange, Tangshan Vocational and Technical College, Hebei, Tangshan

来源：

IEIE Transactions on Smart Processing and Computing | 2023年 / 12卷 / 04期

关键词：

Bilingual evaluation understudy; Chinese-English translation; Machine translation; Tense recognition; Tense translation;

D O I：

10.5573/IEIESPC.2023.12.4.323

中图分类号：

学科分类号：

摘要：

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 short-term 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 comparison 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. Copyrights © 2023 The Institute of Electronics and Information Engineers.

引用

页码：323 / 328

页数：5

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