@inproceedings{fan-etal-2019-strategies,
title = "Strategies for Structuring Story Generation",
author = "Fan, Angela and
Lewis, Mike and
Dauphin, Yann",
editor = "Korhonen, Anna and
Traum, David and
M\`arquez, Llu\'\i s",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1254/",
doi = "10.18653/v1/P19-1254",
pages = "2650--2660",
abstract = "Writers often rely on plans or sketches to write long stories, but most current language models generate word by word from left to right. We explore coarse-to-fine models for creating narrative texts of several hundred words, and introduce new models which decompose stories by abstracting over actions and entities. The model first generates the predicate-argument structure of the text, where different mentions of the same entity are marked with placeholder tokens. It then generates a surface realization of the predicate-argument structure, and finally replaces the entity placeholders with context-sensitive names and references. Human judges prefer the stories from our models to a wide range of previous approaches to hierarchical text generation. Extensive analysis shows that our methods can help improve the diversity and coherence of events and entities in generated stories."
}
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%0 Conference Proceedings
%T Strategies for Structuring Story Generation
%A Fan, Angela
%A Lewis, Mike
%A Dauphin, Yann
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluí s.
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F fan-etal-2019-strategies
%X Writers often rely on plans or sketches to write long stories, but most current language models generate word by word from left to right. We explore coarse-to-fine models for creating narrative texts of several hundred words, and introduce new models which decompose stories by abstracting over actions and entities. The model first generates the predicate-argument structure of the text, where different mentions of the same entity are marked with placeholder tokens. It then generates a surface realization of the predicate-argument structure, and finally replaces the entity placeholders with context-sensitive names and references. Human judges prefer the stories from our models to a wide range of previous approaches to hierarchical text generation. Extensive analysis shows that our methods can help improve the diversity and coherence of events and entities in generated stories.
%R 10.18653/v1/P19-1254
%U https://aclanthology.org/P19-1254/
%U https://doi.org/10.18653/v1/P19-1254
%P 2650-2660
Markdown (Informal)
[Strategies for Structuring Story Generation](https://aclanthology.org/P19-1254/) (Fan et al., ACL 2019)
ACL
- Angela Fan, Mike Lewis, and Yann Dauphin. 2019. Strategies for Structuring Story Generation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2650–2660, Florence, Italy. Association for Computational Linguistics.