This paper proposes a new route for applying the generative adversarial nets (GANs) to NLP tasks (taking the neural machine translation as an instance) and the widespread perspective that GANs can't work well in the NLP area turns out to be unreasonable. In this work, we build a conditional sequence generative adversarial net which comprises of two adversarial sub models, a generative model (generator) which translates the source sentence into the target sentence as the traditional NMT models do and a discriminative model (discriminator) which discriminates the machine-translated target sentence from the human-translated sentence. From the perspective of Turing test, the proposed model is to generate the translation which is indistinguishable from the human-translated one. Experiments show that the proposed model achieves significant improvements than the traditional NMT model. In Chinese-English translation tasks, we obtain up to +2.0 BLEU points improvement. To the best of our knowledge, this is the first time that the quantitative results about the application of GANs in the traditional NLP task is reported. Meanwhile, we present detailed strategies for GAN training. In addition, We find that the discriminator of the proposed model shows great capability in data cleaning.

该论文提出了一种将 GANs 应用于 NMT 的方法，其通过生成对抗网络实现机器翻译任务中句子的生成，并使用 BLEU 评估模型表现，实验证明该方法在英德和中英翻译任务上均优于传统的 RNNSearch 和最新的 Transformer 模型。

使用条件序列生成对抗网络提升神经机器翻译