Open Access

ARTICLE

Neural Machine Translation by Fusing Key Information of Text

by Shijie Hu¹, Xiaoyu Li^1,*, Jiayu Bai¹, Hang Lei¹, Weizhong Qian¹, Sunqiang Hu¹, Cong Zhang², Akpatsa Samuel Kofi¹, Qian Qiu^2,3, Yong Zhou⁴, Shan Yang⁵

1 School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, China
2 Science and Technology on Altitude Simulation Laboratory, Sichuan Gas Turbine Establishment Aero Engine Corporation of China, Mianyang, 621000, China
3 School of Power and Energy, Northwestern Polytechnical University, Xi’an, 710072, China
4 School of Computer Science, Southwest Petroleum University, Chengdu, 610500, China
5 Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, Jackson, MS 39217, USA

* Corresponding Author: Xiaoyu Li. Email:

Computers, Materials & Continua 2023, 74(2), 2803-2815. https://doi.org/10.32604/cmc.2023.032732

Received 27 May 2022; Accepted 12 July 2022; Issue published 31 October 2022

Abstract

When the Transformer proposed by Google in 2017, it was first used for machine translation tasks and achieved the state of the art at that time. Although the current neural machine translation model can generate high quality translation results, there are still mistranslations and omissions in the translation of key information of long sentences. On the other hand, the most important part in traditional translation tasks is the translation of key information. In the translation results, as long as the key information is translated accurately and completely, even if other parts of the results are translated incorrect, the final translation results’ quality can still be guaranteed. In order to solve the problem of mistranslation and missed translation effectively, and improve the accuracy and completeness of long sentence translation in machine translation, this paper proposes a key information fused neural machine translation model based on Transformer. The model proposed in this paper extracts the keywords of the source language text separately as the input of the encoder. After the same encoding as the source language text, it is fused with the output of the source language text encoded by the encoder, then the key information is processed and input into the decoder. With incorporating keyword information from the source language sentence, the model’s performance in the task of translating long sentences is very reliable. In order to verify the effectiveness of the method of fusion of key information proposed in this paper, a series of experiments were carried out on the verification set. The experimental results show that the Bilingual Evaluation Understudy (BLEU) score of the model proposed in this paper on the Workshop on Machine Translation (WMT) 2017 test dataset is higher than the BLEU score of Transformer proposed by Google on the WMT2017 test dataset. The experimental results show the advantages of the model proposed in this paper.

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Keywords

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