Controlled Cue Generation for Play Scripts
This work addresses the need for automated cue generation in specialized domains like play scripts, but it is incremental as it applies existing controlled text generation methods to a new task.
The paper tackles the problem of generating theatrical cues from dialogues in play scripts, using a large-scale dataset and a language model conditioned on a discriminator, with results showing successful generation of plausible and attribute-controlled texts.
In this paper, we use a large-scale play scripts dataset to propose the novel task of theatrical cue generation from dialogues. Using over one million lines of dialogue and cues, we approach the problem of cue generation as a controlled text generation task, and show how cues can be used to enhance the impact of dialogue using a language model conditioned on a dialogue/cue discriminator. In addition, we explore the use of topic keywords and emotions for controlled text generation. Extensive quantitative and qualitative experiments show that language models can be successfully used to generate plausible and attribute-controlled texts in highly specialised domains such as play scripts. Supporting materials can be found at: https://catlab-team.github.io/cuegen.