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Evaluation of Machine Translation Quality Based on Neural Network and Its Application on Foreign Language Education

Published: 14 March 2022 Publication History

Abstract

The form of communication at home and abroad requires translators to constantly develop new professional skills and language skills to meet the needs of translation, At present, neural network translation has become the mainstream of machine translation technology. Based on this, this paper analyzes the quality of machine translation based on neural network and its impact on foreign language education. Through the combination of questionnaire survey and experimental comparison, this paper studies the use and response of machine translation in students' daily learning through questionnaire survey the differences between traditional machine translation and neural network-based machine translation are analyzed based on the combination of questionnaire and experiment. From the data analysis, we can see that the accuracy and efficiency of neural network machine translation are improved. When the vocabulary is 500, the accuracy is increased by 8%, when the time is increased by 1 s, when the vocabulary is 1000, the accuracy is increased by 11%, when the time is increased by 2.3S, 1500 words The accuracy of convergence time increased by 9%, the time increased by 3.4s, the accuracy of 2000 vocabulary increased by 9%, and the time increased by 4.5s.

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  • (2023)A Deep Learning-Based Intelligent Quality Detection Model for Machine TranslationIEEE Access10.1109/ACCESS.2023.330539711(89469-89477)Online publication date: 2023
  1. Evaluation of Machine Translation Quality Based on Neural Network and Its Application on Foreign Language Education

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      cover image ACM Other conferences
      AIAM2021: 2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture
      October 2021
      3136 pages
      ISBN:9781450385046
      DOI:10.1145/3495018
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Published: 14 March 2022

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      • (2023)A Deep Learning-Based Intelligent Quality Detection Model for Machine TranslationIEEE Access10.1109/ACCESS.2023.330539711(89469-89477)Online publication date: 2023

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