Generative Adversarial Nets (GAN) has been successfully introduced to unconditional generating text to alleviate exposure bias. However, the discriminator in this model only evaluates the entire sequence, which causes feedback sparsity and mode collapse. To tackle these problems, we propose a novel mechanism. The mechanism first segments the entire sequence into several subsequences. Then, these subsequences, together with the entire sequence, are evaluated individually by the discriminator. Finally, these feedback signals are all used to guide the learning of GAN. This mechanism learns the generation of both the entire sequence and the subsequences simultaneously. Learning to generate subsequences is easy and is helpful in generating an entire sequence. It is easy to improve the existing GAN-based models with this mechanism. Although Li et al. (2017) segments the generated responses in a conditional text generation task, i.e., a dialogue system, they conclude it is weaker than the Monte Carlo search. However, for unconditional text generation, we observe that adversarial learning on subsequences works well. We rebuild three previous models with our mechanism, and the experimental results on two benchmark datasets show these models are improved greatly and outperform the state-of-the-art model.

生成对抗网络（GAN）已成功引入无条件生成文本以减轻曝光偏差。但是，该模型中的鉴别器仅评估整个序列，这会导致反馈稀疏和模式崩溃。为了解决这些问题，我们提出了一种新颖的机制。该机制首先将整个序列分成几个子序列。然后，由鉴别器分别评估这些子序列以及整个序列。最后，这些反馈信号全部用于指导GAN的学习。该机制同时学习整个序列和子序列的生成。学习生成子序列很容易，并且有助于生成整个序列。使用这种机制很容易改进现有的基于GAN的模型。虽然李等。（2017年）在条件文本生成任务（即对话系统）中对生成的响应进行了细分，他们得出结论认为，此响应比蒙特卡洛搜索要弱。但是，对于无条件文本生成，我们观察到对子序列的对抗学习效果很好。我们用我们的机制重建了之前的三个模型，并且在两个基准数据集上的实验结果表明，这些模型得到了极大的改进，并且性能优于最新模型。