English-Chinese News Text Translation Based on Deep Learning

Huanqi Cao

doi:10.47297/wspiedWSP2516-250003.20220610

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English-Chinese News Text Translation Based on Deep Learning

6卷第10期

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English-Chinese News Text Translation Based on Deep Learning
English-Chinese News Text Translation Based on Deep Learning
School of Electronic Engineering Beijing Jiaotong University, Beijing 100044, P.R. China
2022年6卷第10期页码：16-21
纸质出版日期： 2022-10-25 ，
DOI：10.47297/wspiedWSP2516-250003.20220610
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English-Chinese News Text Translation Based on Deep Learning[J]. 国际教育与发展（英文版）, 2022,6(10):16-21.

Cao Huanqi.English-Chinese News Text Translation Based on Deep Learning[J].Journal of International Education and Development,2022,06(10):16-21.
English-Chinese News Text Translation Based on Deep Learning[J]. 国际教育与发展（英文版）, 2022,6(10):16-21. DOI： 10.47297/wspiedWSP2516-250003.20220610.

Cao Huanqi.English-Chinese News Text Translation Based on Deep Learning[J].Journal of International Education and Development,2022,06(10):16-21. DOI： 10.47297/wspiedWSP2516-250003.20220610.

摘要

Abstract

This paper aims to establish a dataset of English and Chinese news texts and conduct deep learning based on transformer model to explore the translation quality of news texts in the application of deep learning. In this work

Transformer model is used to be the main methods to process data between two different languages's news text in the whole deep learning experiment. The experimental results show that for English and Chinese news texts

it has good learning effects

which greatly improved translation efficiency.

关键词

Keywords

TransformerNatural language processing

references

A. Vaswani, N. Shazeer, N. Parmar, et al. (2017) .Attention is all you need.In: Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, Long Beach, CA, pp. 5998-6008.

J. Devlin, M. W. Chang, K. Lee, et al. (2019). BERT: pre-training of deep bidirectional transformers for language understanding. In: the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Minneapolis, MN, 1:4171-4186.

X. Song, A. Salcianu, Y. Song, et al. (2021) .Fast word-piece tokenization. In: the 2021 Conference on Empirical Methods in Natural Language Processing, Punta Cana, pp. 2089-2103.

C. Park, C. Lee, Y. W. Yang, et al. (2020) . Ancient Korean neural machine translation. IEEE Access, 8:116617-116625.

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