[Submitted on 6 May 2020]
Abstract: 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.
Submission history
From: Deajin Jo [view email]
[v1]
Wed, 6 May 2020 13:10:43 UTC (331 KB)
