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.

中文翻译：

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基于递归递归神经网络的韩汉协同翻译方法探索

为了实现机器学习下韩语协同翻译中抽象动力学的历史序列建立，并在翻译解码过程中在递归模型中动态生成翻译解码树的抽象表示。结合两种神经网络的优势，构建了递归递归神经网络模型，该模型不仅可以利用传统的机器翻译功能对翻译过程进行建模，而且可以在构建过程中逐步构建翻译候选的抽象表示。翻译，有效地挖掘机器翻译中的重要语言模型和其他全局特征。本文已经训练了韩汉翻译词汇的数量，句子的长度和语言对。根据测试结果，该模型可以有效提高机器翻译模型的性能。另外，在调整前序词序的基础上，优化了递归递归神经网络模型，并显着提高了机器翻译的性能。