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        <title>Generating Diverse Story Continuations with Controllable Semantics</title>
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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.</abstract>
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%0 Conference Proceedings
%T Generating Diverse Story Continuations with Controllable Semantics
%A Tu, Lifu
%A Ding, Xiaoan
%A Yu, Dong
%A Gimpel, Kevin
%Y Birch, Alexandra
%Y Finch, Andrew
%Y Hayashi, Hiroaki
%Y Konstas, Ioannis
%Y Luong, Thang
%Y Neubig, Graham
%Y Oda, Yusuke
%Y Sudoh, Katsuhito
%S Proceedings of the 3rd Workshop on Neural Generation and Translation
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong
%F tu-etal-2019-generating
%X 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.
%R 10.18653/v1/D19-5605
%U https://aclanthology.org/D19-5605
%U https://doi.org/10.18653/v1/D19-5605
%P 44-58

Markdown (Informal)

[Generating Diverse Story Continuations with Controllable Semantics](https://aclanthology.org/D19-5605) (Tu et al., NGT 2019)

• Generating Diverse Story Continuations with Controllable Semantics (Tu et al., NGT 2019)

ACL

• Lifu Tu, Xiaoan Ding, Dong Yu, and Kevin Gimpel. 2019. Generating Diverse Story Continuations with Controllable Semantics. In Proceedings of the 3rd Workshop on Neural Generation and Translation, pages 44–58, Hong Kong. Association for Computational Linguistics.