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arXiv · 1909.13434

Generating Diverse Story Continuations with Controllable Semantics

Lifu Tu·Xiaoan Ding·Dong Yu·Kevin Gimpel
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Abstract

We propose a simple and effective modeling framework for controlled generation of multiple, diverse outputs. We focus on the setting of generating the next sentence of a story given its context. As controllable dimensions, we consider several sentence attributes, including sentiment, length, predicates, frames, and automatically-induced clusters. Our empirical results demonstrate: (1) our framework is accurate in terms of generating outputs that match the target control values; (2) our model yields increased maximum metric scores compared to standard n-best list generation via beam search; (3) controlling generation with semantic frames leads to a stronger combination of diversity and quality than other control variables as measured by automatic metrics. We also conduct a human evaluation to assess the utility of providing multiple suggestions for creative writing, demonstrating promising results for the potential of controllable, diverse generation in a collaborative writing system.

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Lifu Tu, Xiaoan Ding, Dong Yu, Kevin Gimpel. 2020-06-02. Generating Diverse Story Continuations with Controllable Semantics. https://arxiv.org/abs/1909.13434

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