Intelligent English translation system based on evolutionary multi-objective optimization algorithm

Issue title: Meta-Heuristic Techniques for Solving Computational Engineering Problems: Challenges and New Research Directions

Guest editors: Suresh Chandra Satapathy, Rashmi Agrawal and Vicente García Díaz

Article type: Research Article

Authors: Song, Xin^{; *}

Affiliations: Taiyuan University, Taiyuan, Shanxi, China

Correspondence: [*] Corresponding author. Xin Song, Taiyuan University, Taiyuan, Shanxi, China. E-mail: [email protected].

Abstract: The difficulty of obtaining the characteristics of the corpus database of neural machine translation is a factor hindering its development. In order to improve the effect of English intelligent translation, based on the machine learning algorithm, this paper improves the multi-objective optimization algorithm to construct a model based on the English intelligent translation system. Moreover, this paper uses parallel corpus and monolingual corpus for model training and uses semi-supervised neural machine translation method to analyze the data processing path in detail and focuses on the analysis of node distribution and data processing flow. In addition, this paper introduces data-related regularization items through the probabilistic nature of the neural machine translation model and applies it to the monolingual corpus to help the training of the neural machine translation model. Finally, this paper designs experiments to verify the performance of this model. The research results show that the translation model constructed in this paper is highly intelligent and can meet actual translation needs.

Keywords: Improved algorithm, machine learning, multi-objective optimization algorithm, English translation

DOI: 10.3233/JIFS-189469

Journal: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 6327-6337, 2021