@inproceedings{goldfarb-tarrant-etal-2020-content,
title = "Content Planning for Neural Story Generation with Aristotelian Rescoring",
author = "Goldfarb-Tarrant, Seraphina and
Chakrabarty, Tuhin and
Weischedel, Ralph and
Peng, Nanyun",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.351",
doi = "10.18653/v1/2020.emnlp-main.351",
pages = "4319--4338",
abstract = "Long-form narrative text generated from large language models manages a fluent impersonation of human writing, but only at the local sentence level, and lacks structure or global cohesion. We posit that many of the problems of story generation can be addressed via high-quality content planning, and present a system that focuses on how to learn good plot structures to guide story generation. We utilize a plot-generation language model along with an ensemble of rescoring models that each implement an aspect of good story-writing as detailed in Aristotle{'}s Poetics. We find that stories written with our more principled plot-structure are both more relevant to a given prompt and higher quality than baselines that do not content plan, or that plan in an unprincipled way.",
}
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<abstract>Long-form narrative text generated from large language models manages a fluent impersonation of human writing, but only at the local sentence level, and lacks structure or global cohesion. We posit that many of the problems of story generation can be addressed via high-quality content planning, and present a system that focuses on how to learn good plot structures to guide story generation. We utilize a plot-generation language model along with an ensemble of rescoring models that each implement an aspect of good story-writing as detailed in Aristotle’s Poetics. We find that stories written with our more principled plot-structure are both more relevant to a given prompt and higher quality than baselines that do not content plan, or that plan in an unprincipled way.</abstract>
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%0 Conference Proceedings
%T Content Planning for Neural Story Generation with Aristotelian Rescoring
%A Goldfarb-Tarrant, Seraphina
%A Chakrabarty, Tuhin
%A Weischedel, Ralph
%A Peng, Nanyun
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F goldfarb-tarrant-etal-2020-content
%X Long-form narrative text generated from large language models manages a fluent impersonation of human writing, but only at the local sentence level, and lacks structure or global cohesion. We posit that many of the problems of story generation can be addressed via high-quality content planning, and present a system that focuses on how to learn good plot structures to guide story generation. We utilize a plot-generation language model along with an ensemble of rescoring models that each implement an aspect of good story-writing as detailed in Aristotle’s Poetics. We find that stories written with our more principled plot-structure are both more relevant to a given prompt and higher quality than baselines that do not content plan, or that plan in an unprincipled way.
%R 10.18653/v1/2020.emnlp-main.351
%U https://aclanthology.org/2020.emnlp-main.351
%U https://doi.org/10.18653/v1/2020.emnlp-main.351
%P 4319-4338
Markdown (Informal)
[Content Planning for Neural Story Generation with Aristotelian Rescoring](https://aclanthology.org/2020.emnlp-main.351) (Goldfarb-Tarrant et al., EMNLP 2020)
ACL