School of Foreign Languages, Henan University of Animal Husbandry and Economy, Zhengzhou, Henan 450046, China.
Comput Intell Neurosci. 2022 Jun 16;2022:9702112. doi: 10.1155/2022/9702112. eCollection 2022.
With the increasing communication between countries, machine translation has become an important way of communication between ethnic groups in different language systems. In order to further improve the coverage mechanism and vocabulary translation quality of the machine translation model of neural network, this paper will study the machine translation English lexical analysis system based on simple recurrent neural network. In order to improve the effect of word alignment, marking and attention mechanism are introduced into the English lexical analysis model. The experimental results show that the tagging of named entity words in the system can reduce the problem of unregistered words. Combined with the attention mechanism, it can significantly improve the effect of word alignment and label recall. It not only improves the controllability of translation but also has a positive impact on the quality of translation and its application effect in specific scenes. The evaluation of translation quality indicators shows that the system can effectively improve the accuracy and quality of Chinese-English translation.
随着国家间交流的增加，机器翻译已成为不同语言系统的民族之间交流的重要方式。为了进一步提高神经网络机器翻译模型的覆盖机制和词汇翻译质量，本文将研究基于简单循环神经网络的机器翻译英语词汇分析系统。为了提高词对齐的效果，在英语词汇分析模型中引入了标记和注意力机制。实验结果表明，系统中命名实体词的标记可以减少未注册词的问题。结合注意力机制，可以显著提高词对齐和标签召回的效果。它不仅提高了翻译的可控性，而且对翻译的质量及其在特定场景中的应用效果有积极影响。翻译质量指标的评估表明，该系统可以有效地提高汉英翻译的准确性和质量。
