Generating Diverse Story Continuations with Controllable Semantics

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.

arXiv commentWNGT 2019

References in corpus (5)

• Sequence to Sequence Learning with Neural Networks
• Style Transfer from Non-Parallel Text by Cross-Alignment
• Narrative Planning: Balancing Plot and Character
• Mutual Information and Diverse Decoding Improve Neural Machine Translation
• Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models