Shaping the Narrative Arc: An Information-Theoretic Approach to Collaborative Dialogue
By Kory Wallace Mathewson, Pablo Samuel Castro, Colin Cherry, George F. Foster, Marc G. Bellemare. 2019
14 citations recorded by the source corpus.
We consider the problem of designing an artificial agent capable of interacting with humans in collaborative dialogue to produce creative, engaging narratives. In this task, the goal is to establish universe details, and to collaborate on an interesting story in that universe, through a series of natural dialogue exchanges. Our model can augment any probabilistic conversational agent by allowing it to reason about universe information established and what potential next utterances might reveal. Ideally, with each utterance, agents would reveal just enough information to add specificity and reduce ambiguity without limiting the conversation. We empirically show that our model allows control over the rate at which the agent reveals information and that doing so significantly improves accuracy in predicting the next line of dialogues from movies. We close with a case-study with four professional theatre performers, who preferred interactions with our model-augmented agent over an unaugmented agent.
Subfields: Artificial Intelligence
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In Artificial Intelligence
Citations in the corpus
- Co-Writing Screenplays and Theatre Scripts with Language Models: Evaluation by Industry Professionals (2022, background): “[68] as an approach to produce creative, engaging dialogue.”