Natural Language Word Prediction Model Based on Multi-Window Convolution and Residual Network
Jingyun Yang, Hengjun Wang, Kexiang Guo
Abstract
In this paper, we propose MCNN-ReMGU model based on multi-window convolution and residual-connected minimal gated unit (MGU) network for the natural language word prediction. First, the convolution kernels with different sizes are used to extract the local feature information of different graininess between the word sequences. Then, the extracted features are fed to the residual-connected MGU network. Finally, the prediction results are output by the SoftMax layer. Through the residual-connection processing of MGU network in the model, not only the problems of vanishing gradient and network degradation are effectively solved, but also the long-term dependence between word sequences is effectively extracted to predict the next word accurately. Meanwhile, the introduction of the convolution kernel in a convolutional neural network (CNN) enables the feature information between word sequences to be extracted more fully. The experimental results on the Penn Treebank and WikiText-2 datasets show that the proposed method has certain advantages in the word prediction task.