Exploration on Korean-Chinese collaborative translation method based on recursive recurrent neural network

Abstract

In order to realize the historical sequence establishment of abstract dynamics in the cooperative translation of Korean language under machine learning and generate an abstract representation dynamically of the translation decoding tree in the recursive model during translation decoding. Combining the advantages of the two kinds of neural networks, this paper constructs a recursive recurrent neural network model, which can not only model the translation process by using the traditional machine translation features but also gradually construct the abstract representation of translation candidates in the process of translation, mining the important language model and other global features in machine translation effectively. This paper has trained the number of Korean-Chinese translation vocabulary, sentence length, and language pairs. Based on the test results, the model can effectively improve the performance of the machine translation model. In addition, based on the adjustment of pre-order word order to optimized the recursive recurring neural network model, and improved the performance of machine translation significantly.