Exploration of Strategies and Technological Paths to Improve the Quality of English Literature Translation Empowered by Artificial Intelligence

Abstract

Under the environment of artificial intelligence, the construction of an intelligent English translation work system that meets the needs of society is an effective way to improve the efficiency of English translation work. The study discusses the strategy to improve the quality of English literary translation, and gives the technical path to improve the quality of English literary translation through the construction of the corpus of literary field and the English machine translation model based on RNN migration learning. The model combines RNN neural network with Word2vec, transfer learning technology and attention mechanism, which can effectively improve the translation quality of English literature machine translation. The experimental analysis verifies the effectiveness of the corpus in the literature domain and reveals the improvement effect of this paper's model on the performance of the English literature translation model and the translation quality, with its BLEU average value improved by 31.22%~44.18% compared with the comparison method, and it performs the best in the English literature long-sentence translation test. The model in this paper significantly improves the translation quality and can be used for actual English literature machine translation.