Affiliations: [a] Foreign Language School, Hunan University of Science and Engineering, Yongzhou, Hunan, China | [b] School of Information Science and Engineering, Hunan Women’s University, Changsha, Hunan, China
Correspondence:
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Corresponding author: Yipeng Luo, School of Information Science and Engineering, Hunan Women’s University, No. 160, Zhongyi 1st Road, Changsha, Hunan 410004, China. E-mail: [email protected].
Abstract: There is an increasing demand for high-quality translations in the realm of intelligent English translation. This paper optimized the traditional Transformer algorithm by enhancing position coding and the softmax layer. Bidirectional long short-term memory (BiLST) was employed to realize position encoding, capturing both contextual and positional information simultaneously. Additionally, the softmax function was replaced with the sparsemax function to obtain sparser results. The translation performance of some algorithms on Chinese and English datasets was compared and analyzed. It was found that that the optimized Transformer algorithm performed better than the RNNSearch, ConvS2S, and Transformer-base algorithms in terms of bilingual evaluation understudy (BLEU) score on the test set. It achieved an average BLEU score of 23.72, representing an improvement of 1.56 over the RNNSearch algorithm, 1.17 over the ConvS2S algorithm, and 0.73 over the Transformer-base algorithm. The parameter quantity of the optimized algorithm was 6.83 M, which was 0.05 M higher than the Transformer-base algorithm. Furthermore, its running time was 4,862.76 s, showing a marginal increase of 0.21% compared to the Transformer-base algorithm. These findings validate the reliability of the optimized Transformer algorithm for intelligent English translation and its potential practical application.
Keywords: Transformer algorithm, intelligent translation, position coding, softmax function
