With the increase of translation demand, the advancement of information technology, the development of linguistic theories and the progress of natural language understanding models in artificial intelligence research, machine translation has gradually gained worldwide attention. However, at present, machine translation research still has problems such as insufficient bilingual data and lack of effective feature representation, which affects the further improvement of key modules of machine translation such as word alignment, sequence adjustment and translation modelling. The effect of machine translation is still unsatisfactory. As a new machine learning method, deep neural network can automatically learn abstract feature representation and establish a complex mapping relationship between input and output signals, which provides a new idea for statistical machine translation research. Firstly, the multi-layer neural network and the undirected probability graph model are combined, and the similarity and context information of vocabulary are effectively utilized to model the word alignment more fully, and the word alignment model named NNWAM is constructed. Secondly, the low dimension will be used. The feature representation is combined with other features into a linearly ordered pre-ordering model to construct the pre-ordering model named NNPR. Finally, the word alignment model and the pre-ordering model are combined in the same deep neural network framework to form DNNAPM, a statistical machine translation model based on deep neural networks. The experimental results show that the statistical machine translation model based on deep neural network has better effect, faster convergence and better reliability than the comparison model algorithm.

中文翻译：

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基于深度神经网络的统计机器翻译模型研究

随着翻译需求的增加、信息技术的进步、语言学理论的发展以及人工智能研究中自然语言理解模型的进步，机器翻译逐渐受到全世界的关注。然而，目前机器翻译研究还存在双语数据不足、缺乏有效特征表示等问题，影响了词对齐、序列调整、翻译建模等机器翻译关键模块的进一步完善。机器翻译的效果还是不尽如人意。作为一种新的机器学习方法，深度神经网络可以自动学习抽象的特征表示，并在输入和输出信号之间建立复杂的映射关系，为统计机器翻译研究提供了新思路。首先将多层神经网络与无向概率图模型相结合，有效利用词汇的相似性和上下文信息对词对齐进行更充分的建模，构建了词对齐模型NNWAM。其次，将使用低维。将特征表示与其他特征组合成一个线性排序的预排序模型，构建名为 NNPR 的预排序模型。最后将词对齐模型和预排序模型组合在同一个深度神经网络框架中，形成DNNAPM，一种基于深度神经网络的统计机器翻译模型。