Abstract:

Anti-neural machine translation method is a hot machine translation algorithm at present, but the translation accuracy of traditional anti-neural network model depends on a large number of corpus data sets, and the model training takes a lot of time. When the corpus is scarce, the model translation quality is poor. Aiming at the shortcomings of traditional anti-neural network machine translation algorithm, this paper combines variational algorithm with anti-neural network to train corpus data. Experimental results show that the BLEU value of the variational anti-neural network translation is obviously improved compared with the traditional translation algorithm.When the number of training corpus is scarce, the BLEU value of the model is greatly improved compared with other algorithms, which shows that the proposed algorithm model can effectively shorten the training time of data, elevate the training accuracy of data and improve the translation quality of sentences.

Key words: machine translation, fight against neural networks, variational Bayesian, neural network algorithm, English-Chinese translation, confrontational learning, BLEU, natural language processing