Generative Adversarial Network-Based Short Sequence Machine Translation from Chinese to English

Abstract

With the acceleration of economic globalization, the economic contact, information exchange, and financial integration between countries become more and more frequent. In this context, the communication between different languages is also closer, so accurate translation between languages is of great significance. However, existing methods give little thought to short sequence machine translation from Chinese to English. This paper designs a generative adversarial network to solve the above problem. First, a conditional sequence generating adversarial net is constructed, which includes two adversarial submodels: a generator and a discriminator. The generator is designed to generate sentences that are difficult to distinguish from human-translated sentences, and the discriminator is designed to distinguish the sentences generated by the generator from human-translated sentences. In addition, static sentence-level BLEU values will be used as reinforcement targets for the generator. During training, both dynamic discriminators and static BLEU targets are used to evaluate the generated sentences, and the evaluation results are fed back to the generator to guide the generator's learning. Finally, experimental results on English-Chinese translation dataset show that the translation effect is improved by more than 8% compared with the traditional neural machine translation model based on recurrent neural network (RNN) after the introduction of generative adversative network.