Rural Novel Translation Methods Based on Bidirectional GRU Neural Network Machine Model

Abstract

Abstract: In order to improve the accuracy and efficiency of machine translation of novels, an RNN neural network framework based on end to end is proposed to study the rural novel translation methods by using the Chinese to English machine model. By analyzing the translation principles and performance of the RNN-NMT model, WordNet Semantic Similarity model, GRU-LM model and BiGRU-LM model, the new BiGRU-LM-Attention machine model is established to carry out translation testing and quality performance evaluation. Tests prove that the BLEU value of the new model is higher than these of other models; at the same time, on the quality performance of example translation, the accuracy rate of new model is ahead of 4 online translating tools in terms of semantic recognition, dialects, special nouns, slang and flexible recognition of passive voice, indicating that the improved neural machine model in this paper can adapt to the characteristic language of the novel and effectively improve the translation quality, which is of significance of Chinese culture transmission.

Key words: neural machine translation, bidirectional GRU, attention mechanism, Happy Dreams, rural novel

SUN Li-li, GUO Lin, WEN Xu, ZHANG Wen-nuo. Rural Novel Translation Methods Based on Bidirectional GRU Neural Network Machine Model[J]. Computer and Modernization, 2021, 0(04): 27-31.

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