Neural machine translation has been widely concerned in recent years. The traditional sequential neural network framework of English translation has obvious disadvantages because of its poor ability to capture long-distance information, and the current improved framework, such as the recurrent neural network, still cannot solve this problem very well. In this paper, we propose a hybrid neural network that combines the convolutional neural network (CNN) and long short-term memory (LSTM) and introduce the attention mechanism based on the encoder-decoder structure to improve the translation accuracy, especially for long sentences. In the experiment, this model is implemented based on TensorFlow, and the results show that the BLEU value of the proposed method is obviously improved compared with the traditional machine learning model, which proves the effectiveness of our method in English-Chinese translation.

中文翻译：

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基于混合神经网络的高效英语翻译方法与分析

近年来，神经机器翻译受到广泛关注。传统的顺序神经网络英语翻译框架由于捕获远程信息的能力较弱而具有明显的缺点，而当前改进的框架（例如递归神经网络）仍然不能很好地解决该问题。本文提出了一种将卷积神经网络（CNN）与长短期记忆（LSTM）相结合的混合神经网络，并介绍了基于编码器-解码器结构的注意力机制，以提高翻译准确度，特别是对于长句子的翻译。在实验中，该模型是基于TensorFlow实现的，结果表明，与传统的机器学习模型相比，该方法的BLEU值有明显的提高，