基于GAN模型优化的神经机器翻译

针对神经机器翻译和人工翻译性能的差异最小化、训练语料不足问题, 提出了一种基于生成对抗网络的神经机器翻译改进方法.首先对目标端句子序列添加微小的噪声干扰, 通过编码器还原原始句子形成新的序列; 其次将编码器的处理结果交给判别器和解码器进一步处理, 在训练过程中, 判别器和双语评估基础值(BLEU)目标函数用于评估生成的句子, 并将结果反馈给生成器, 引导生成器学习及优化.实验结果表明, 对比传统的神经机器翻译模型, 基于GAN模型的方法极大地提高了模型的泛化能力和翻译的精度.

Optimization of Neural Machine Translation with GAN Model

To minimize the performance difference between neural machine translation (NMT) and human translation and solve the problem of insufficient training corpora, this study proposes an improved NMT method based on the generative adversarial network (GAN). First, the sentence sequence of the target end is added with small noise interference, and then the original sentence is restored by the encoder to form a new sequence. Secondly, the results of the encoder are presented to the discriminator and decoder for further processing. In the training process, the discriminator and the bilingual evaluation understudy (BLEU) objective function are employed to evaluate the generated sentences, and the results are fed back to the generator to instruct its learning and optimization. The experimental results demonstrate that compared with the traditional NMT model, the GAN-based model greatly improves the generalization ability and translation accuracy of the model.