基于GAN模型优化的神经机器翻译

在机器翻译任务中,输入端的一些微小的干扰信息,可能引起NMT的模型翻译性能的下降。该文提出了一种融入对抗学习的神经机器翻译方法。给出一个源句子序列,构造了一个将源句子添加了微小噪声的新序列,并且两者的语义相近。然后把这两个序列交由编码器处理,产生各自的向量表示;并将处理结果交给判别器和解码器做进一步处理,最后比较加入噪声前后的翻译性能。实验表明,在多个语言对的翻译任务上,使用该模型的方法不仅提升了翻译性能,而且对噪声输入也表现出了鲁棒性。

Neural Machine Translation Based on GAN Optimization

A subtle perturbation in the input can decline the performance of the Neural Machine Translation (NMT). This work proposes a neural machine translation method incorporating adversarial learning. Given a source sentence sequence, we construct a new sequence by adding subtle noise to the source sentence, and the two sequences have the similar semantics. Then we submit the two sentences of the last step to encoder so as to generate their vector representations respectively. Next, we submit the processing results to Generator and Discriminator for further processing. Lastly, we compare the translation performance before and after adding the noise. The final results show that the method of using this model both improves the translation performance, and shows the robustness to the noise input.