MaskGAN opens the query for the conditional language model by filling in the blanks between the given tokens. In this paper, we focus on addressing the limitations caused by having to specify blanks to be filled. We decompose conditional text generation problem into two tasks, make-a-blank and fill-in-the-blank, and extend the former to handle more complex manipulations on the given tokens. We cast these tasks as a hierarchical multi agent RL problem and introduce a conditional adversarial learning that allows the agents to reach a goal, producing realistic texts, in cooperative setting. We show that the proposed model produces text samples without compromising the performance in terms of quality and diversity.

翻译：MaskGAN 通过填充给定标牌之间的空白来打开有条件语言模式的查询。 在本文中, 我们侧重于解决因必须指定要填满的空白而带来的限制。 我们将有条件文本生成问题分解成两个任务, 即制造空白和填满空白, 并将前者扩大到处理给定标牌上更复杂的操作。 我们将这些任务作为一个等级化的多剂RL问题, 并引入有条件的对抗性学习, 让代理商在合作环境中达到一个目标, 产生现实的文本。 我们显示, 拟议的模型在不损害质量和多样性性能的情况下生成文本样本 。