@inproceedings{sagarkar-etal-2018-quality,
title = "Quality Signals in Generated Stories",
author = "Sagarkar, Manasvi and
Wieting, John and
Tu, Lifu and
Gimpel, Kevin",
editor = "Nissim, Malvina and
Berant, Jonathan and
Lenci, Alessandro",
booktitle = "Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S18-2024",
doi = "10.18653/v1/S18-2024",
pages = "192--202",
abstract = "We study the problem of measuring the quality of automatically-generated stories. We focus on the setting in which a few sentences of a story are provided and the task is to generate the next sentence ({``}continuation{''}) in the story. We seek to identify what makes a story continuation interesting, relevant, and have high overall quality. We crowdsource annotations along these three criteria for the outputs of story continuation systems, design features, and train models to predict the annotations. Our trained scorer can be used as a rich feature function for story generation, a reward function for systems that use reinforcement learning to learn to generate stories, and as a partial evaluation metric for story generation.",
}
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<abstract>We study the problem of measuring the quality of automatically-generated stories. We focus on the setting in which a few sentences of a story are provided and the task is to generate the next sentence (“continuation”) in the story. We seek to identify what makes a story continuation interesting, relevant, and have high overall quality. We crowdsource annotations along these three criteria for the outputs of story continuation systems, design features, and train models to predict the annotations. Our trained scorer can be used as a rich feature function for story generation, a reward function for systems that use reinforcement learning to learn to generate stories, and as a partial evaluation metric for story generation.</abstract>
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%0 Conference Proceedings
%T Quality Signals in Generated Stories
%A Sagarkar, Manasvi
%A Wieting, John
%A Tu, Lifu
%A Gimpel, Kevin
%Y Nissim, Malvina
%Y Berant, Jonathan
%Y Lenci, Alessandro
%S Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F sagarkar-etal-2018-quality
%X We study the problem of measuring the quality of automatically-generated stories. We focus on the setting in which a few sentences of a story are provided and the task is to generate the next sentence (“continuation”) in the story. We seek to identify what makes a story continuation interesting, relevant, and have high overall quality. We crowdsource annotations along these three criteria for the outputs of story continuation systems, design features, and train models to predict the annotations. Our trained scorer can be used as a rich feature function for story generation, a reward function for systems that use reinforcement learning to learn to generate stories, and as a partial evaluation metric for story generation.
%R 10.18653/v1/S18-2024
%U https://aclanthology.org/S18-2024
%U https://doi.org/10.18653/v1/S18-2024
%P 192-202
Markdown (Informal)
[Quality Signals in Generated Stories](https://aclanthology.org/S18-2024) (Sagarkar et al., *SEM 2018)
ACL
- Manasvi Sagarkar, John Wieting, Lifu Tu, and Kevin Gimpel. 2018. Quality Signals in Generated Stories. In Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics, pages 192–202, New Orleans, Louisiana. Association for Computational Linguistics.