Abstract
Automatically generating coherent and semantically meaningful text has many applications in machine translation, dialogue systems, image captioning, etc. Recently, by combining with policy gradient, Generative Adversarial Nets (GAN) that use a discriminative model to guide the training of the generative model as a reinforcement learning policy has shown promising results in text generation. However, the scalar guiding signal is only available after the entire text has been generated and lacks intermediate information about text structure during the generative process. As such, it limits its success when the length of the generated text samples is long (more than 20 words). In this paper, we propose a new framework, called LeakGAN, to address the problem for long text generation. We allow the discriminative net to leak its own high-level extracted features to the generative net to further help the guidance. The generator incorporates such informative signals into all generation steps through an additional Manager module, which takes the extracted features of current generated words and outputs a latent vector to guide the Worker module for next-word generation. Our extensive experiments on synthetic data and various real-world tasks with Turing test demonstrate that LeakGAN is highly effective in long text generation and also improves the performance in short text generation scenarios. More importantly, without any supervision, LeakGAN would be able to implicitly learn sentence structures only through the interaction between Manager and Worker.
Abstract (translated)
自动生成连贯和语义上有意义的文本在机器翻译，对话系统，图像字幕等方面有很多应用。最近，通过与策略梯度相结合，生成对抗网络（GAN）使用区分性模型来指导生成模型的训练为强化学习政策已经在文本生成中显示出有希望的结果。但是，标量指导信号只有在生成完整文本后才可用，并且在生成过程中缺少关于文本结构的中间信息。因此，当生成的文本样本的长度很长（超过20个字）时，它会限制它的成功。在本文中，我们提出了一个名为LeakGAN的新框架来解决长文本生成的问题。我们允许有差别的网络将其自己的高级提取特征泄露给生成网络以进一步帮助指导。生成器通过一个额外的管理器模块将这些信息信号合并到所有生成步骤中，该管理器模块采用当前生成的字的提取特征并输出一个潜在向量来指导工作者模块进行下一代生成。我们通过图灵测试对合成数据和各种实际任务进行的广泛实验证明，LeakGAN在长文本生成中非常有效，并且在短文本生成场景中也提高了性能。更重要的是，没有任何监督，LeakGAN只能通过管理者和工作者之间的交互来隐含地学习句子结构。
URL
https://arxiv.org/abs/1709.08624
