Design of English Translation Model Based on Recurrent Neural Network

Abstract

In order to improve the accuracy and stability of English translation, this paper proposes an English translation model based on recurrent neural network. Based on the end-to-end encoder-decoder architecture, a recursive neural network (RNN) English machine translation model is designed to promote machine autonomous learning features, transform the distributed corpus data into word vectors, and directly map the source language and target language through the recurrent neural network. Selecting semantic errors to construct the objective function during training can well balance the influence of each part of the semantics and fully consider the alignment information, providing a strong guidance for the training of deep recurrent neural networks. The experimental results show that the English translation model based on recurrent neural network has high effectiveness and stability. Compared with the baseline system, it has improved about 1.51–1.86 BLEU scores. Conclusion. The model improves the performance and quality of English machine translation model, and the translation effect is better.

Cross-Language Entity Alignment Based on Dual-Relation Graph and Neighbor Entity Screening

“…Many entity alignment algorithms have been implemented based on graph representation learning, which encodes all entities in a knowledge graph in order to extract the graph features of the entities. Entity alignment based on representation learning has been divided into two categories, namely translation model-based methods [2,13,14] and graph convolutional network (GCN)-based methods [2][3][4]15]. It is worth noting that most of the following methods have used Chinese-English, French-English, and Japanese-English datasets.…”