Optimization of Machine Online Translation System Based on Deep Convolution Neural Network Algorithm.

Juan Zhao

doi:10.1155/2021/7388825

Abstract

In order to effectively optimize the machine online translation system and improve its translation efficiency and translation quality, this study uses the deep separable convolution neural network algorithm to construct a machine online translation model and evaluates the quality on the basis of pseudo data learning. In order to verify the performance of the model, the regression performance experiment of the model, the method performance experiment of generating pseudo data for specific tasks, the sorting task performance experiment of the model, and the machine translation quality comparison experiment are designed. RMSE and MAE were used to evaluate the regression task performance of the model. Spearman rank correlation coefficient and delta AVG value were used to evaluate the sorting task performance of the model. The experimental results show that the MAE and RMSE values of the model are decreased by 2.28% and 1.39%, respectively, compared with the baseline system under the same experimental conditions, and the Spearman and delta AVG values are increased by 132% and 100.7%, respectively, compared with the baseline system. The method of generating pseudo data for specific tasks needs less data and can make the translation system reach a better level faster. When the number of instances is more than 10, the quality score of the model output is higher than that of Google translation whose similarity is more than 0.8.

Highlights

Machine online translation system is to translate a source language into another target language by combining artificial intelligence technology and human language processing technology. e machine online translation system can realize the translation of multiple languages, its efficiency is far higher than that of manual translation, and it is easier to use
Compared with traditional machine learning methods, deep convolution neural network has a deeper network structure, and its operation efficiency and accuracy have significant advantages. ese deep structures closely imitate the biological process of vision. rough the establishment of a system with visual features and a series of “convolution” operations to complete the deep learning, the results show that the extracted features can well represent the quality of the image [5]. e online translation model needs model performance evaluation and translation quality evaluation, and attention should be paid to noise interference in the process of experiment [6]
Performance Evaluation of Deep Convolution Neural Network Algorithm for Machine Online Translation Model Training Methods. is experiment compares the performance of different models in two aspects: regression task and sorting task. e experimental conditions of different models are the same. e Root mean square error (RMSE) and mean absolute error (MAE) performance values of regression task are obtained from the training results of regression task

Summary

Introduction

Machine online translation system is to translate a source language into another target language by combining artificial intelligence technology and human language processing technology. e machine online translation system can realize the translation of multiple languages, its efficiency is far higher than that of manual translation, and it is easier to use. E machine online translation system can realize the translation of multiple languages, its efficiency is far higher than that of manual translation, and it is easier to use. The quality of machine translation is lower than that of manual translation. The accuracy of machine translation is still low [2]. How to improve the accuracy of the machine translation system through deep learning is one of the important research directions. E main content of this study is to establish an Machine online translation model based on the deep separable convolutional neural network algorithm, provide deep learning and training methods, improve the quality of translation system model and translation, and reduce the dependence of the translation system on manual annotation Language translation and image translation are in the research stage. e main content of this study is to establish an Machine online translation model based on the deep separable convolutional neural network algorithm, provide deep learning and training methods, improve the quality of translation system model and translation, and reduce the dependence of the translation system on manual annotation

Methods

Results

Conclusion