Two-Way Neural Network Chinese-English Machine Translation Model Fused with Attention Mechanism

Abstract

This study uses an end-to-end encoder-decoder structure to build a machine translation model, allowing the machine to automatically learn features and transform the corpus data into distributed representations. The word vector uses a neural network to achieve direct mapping. Research on constructing neural machine translation models for different neural network structures. Based on the translation model of the LSTM network, the gate valve mechanism reduces the gradient attenuation and improves the ability to process long-distance sequences. Based on the GRU network structure, the simplified processing is performed on the basis of it, which reduces the training complexity and achieves good performance. Aiming at the problem that some source language sequences of any length in the encoder are encoded into fixed-dimensional background vectors, the attention mechanism is introduced to dynamically adjust the degree of influence of the source language context on the target language sequence, and the translation model’s ability to deal with long-distance dependencies is improved. In order to better reflect the context information, this study further proposes a machine translation model based on two-way GRU and compares and analyzes multiple translation models to verify the effectiveness of the model’s performance improvement.

Research on Neural Network Machine Translation Model Based on Entity Tagging Improvement

“…Sennrich et al [16] proposed an "encoding-decoding" model of neural machine translation and explained the encoding rules. Liang and Du [17] used a convolutional neural network to construct the encoder and a recurrent neural network for the decoder to obtain historical information and process variable-length strings. e current mainstream neural network machine translation approach is an end-to-end codec translation system built using recurrent neural network (RNN) models.…”