RNN Neural Network Model for Chinese-Korean Translation Learning

Abstract

Translation is the expression of words into other forms so that they are mutually understandable. However, the current Chinese-Korean translation information conversion model is not mature enough, and there are many defects and errors. This paper proposes an improved RNN neural network translation model. The translation model proposed in this study is an upgraded RNN neural network (a form of recurrent neural network). A decoder matching mode is included in the model, allowing it to learn both alignment and transformation at the same time. This allows for quicker neural network training, resulting in increased translation speed and efficiency. In addition, this article compares the BLEU scores of the BilingualCorpus Chinese-Korean corpus I, II, and III with those of the standard translation model. The test results suggest that the enhanced RNN model described in this research has an average BLEU score of roughly 45 points. The conventional model’s average BLEU score is just approximately 30 points. The BLEU score of the model in this paper has increased by about 15 points, indicating that the translation quality of the translation model in this paper has been significantly improved.

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