School of Humanities and Social Sciences, Xi'an Polytechnic University, Xi'an City 710048, China.
Shaanxi Contemporary Red Culture Training and Education Center, Xi'an City 710061, China.
Comput Intell Neurosci. 2023 Feb 22;2023:9450816. doi: 10.1155/2023/9450816. eCollection 2023.
At present, complete machine translation (MT) cannot meet the needs of information communication and cultural exchange, and the speed of complete human translation is too slow. Therefore, if MT is used to assist in the process of English-Chinese translation, it can not only prove that machine learning (ML) can translate English to Chinese but also improve the translation efficiency and accuracy of translators through human-machine cooperation. The research on the mutual cooperation between ML and human translation has an important research significance for translation systems. An English-Chinese computer-aided translation (CAT) system is designed and proofread based on a neural network (NN) model. First, it gives a brief overview of CAT. Second, the related theory of the NN model is discussed. An English-Chinese CAT and proofreading system based on the recurrent neural network (RNN) is constructed. Finally, the translation accuracy and proofreading recognition rate of the translation files of 17 different projects under different models are studied and analyzed. The research results reveal that according to the different translation properties of different texts, the average accuracy rate of text translation under the RNN model is 93.96%, and the mean accuracy of text translation under the transformer model is 90.60%. The translation accuracy of the RNN model in the CAT system is 3.36% higher than that of the transformer model. The English-Chinese CAT system based on the RNN model has different proofreading results for sentence processing, sentence alignment, and inconsistency detection of translation files of different projects. Among them, the recognition rate for sentence alignment and the inconsistency detection of English-Chinese translation is high, and the expected effect is achieved. The design of the English-Chinese CAT and proofreading system based on the RNN can make the translation and proofreading be carried out simultaneously, which greatly improves the efficiency of translation work. Meanwhile, the above research methods can improve the problems encountered in the current English-Chinese translation, provide a path for the bilingual translation process, and have certain promotion prospects.
目前，完全的机器翻译（MT）无法满足信息交流和文化交流的需求，而完全的人工翻译速度又太慢。因此，如果将 MT 用于辅助英语到中文的翻译过程，不仅可以证明机器学习（ML）可以将英语翻译为中文，还可以通过人机协作提高翻译员的翻译效率和准确性。对 ML 与人工翻译相互协作的研究对翻译系统具有重要的研究意义。本文设计并校对了一个基于神经网络（NN）模型的英中计算机辅助翻译（CAT）系统。首先，简要概述了 CAT。其次，讨论了 NN 模型的相关理论。构建了基于循环神经网络（RNN）的英中 CAT 和校对系统。最后，研究和分析了在不同模型下 17 个不同项目的翻译文件的翻译准确性和校对识别率。研究结果表明，根据不同文本的不同翻译特性，RNN 模型下文本翻译的平均准确率为 93.96%，Transformer 模型下文本翻译的平均准确率为 90.60%。RNN 模型在 CAT 系统中的翻译准确率比 Transformer 模型高 3.36%。基于 RNN 模型的英中 CAT 系统对不同项目的翻译文件的句子处理、句子对齐和翻译不一致性检测具有不同的校对结果。其中，句子对齐和英中翻译不一致性检测的识别率较高，达到了预期效果。基于 RNN 的英中 CAT 和校对系统的设计可以使翻译和校对同时进行，极大地提高了翻译工作的效率。同时，上述研究方法可以解决当前英汉翻译中遇到的问题，为双语翻译过程提供一种途径，具有一定的推广前景。
