@inproceedings{alabdulkarim-etal-2021-automatic,
title = "Automatic Story Generation: Challenges and Attempts",
author = "Alabdulkarim, Amal and
Li, Siyan and
Peng, Xiangyu",
editor = "Akoury, Nader and
Brahman, Faeze and
Chaturvedi, Snigdha and
Clark, Elizabeth and
Iyyer, Mohit and
Martin, Lara J.",
booktitle = "Proceedings of the Third Workshop on Narrative Understanding",
month = jun,
year = "2021",
address = "Virtual",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.nuse-1.8",
doi = "10.18653/v1/2021.nuse-1.8",
pages = "72--83",
abstract = "Automated storytelling has long captured the attention of researchers for the ubiquity of narratives in everyday life. The best human-crafted stories exhibit coherent plot, strong characters, and adherence to genres, attributes that current states-of-the-art still struggle to produce, even using transformer architectures. In this paper, we analyze works in story generation that utilize machine learning approaches to (1) address story generation controllability, (2) incorporate commonsense knowledge, (3) infer reasonable character actions, and (4) generate creative language.",
}
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<abstract>Automated storytelling has long captured the attention of researchers for the ubiquity of narratives in everyday life. The best human-crafted stories exhibit coherent plot, strong characters, and adherence to genres, attributes that current states-of-the-art still struggle to produce, even using transformer architectures. In this paper, we analyze works in story generation that utilize machine learning approaches to (1) address story generation controllability, (2) incorporate commonsense knowledge, (3) infer reasonable character actions, and (4) generate creative language.</abstract>
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%0 Conference Proceedings
%T Automatic Story Generation: Challenges and Attempts
%A Alabdulkarim, Amal
%A Li, Siyan
%A Peng, Xiangyu
%Y Akoury, Nader
%Y Brahman, Faeze
%Y Chaturvedi, Snigdha
%Y Clark, Elizabeth
%Y Iyyer, Mohit
%Y Martin, Lara J.
%S Proceedings of the Third Workshop on Narrative Understanding
%D 2021
%8 June
%I Association for Computational Linguistics
%C Virtual
%F alabdulkarim-etal-2021-automatic
%X Automated storytelling has long captured the attention of researchers for the ubiquity of narratives in everyday life. The best human-crafted stories exhibit coherent plot, strong characters, and adherence to genres, attributes that current states-of-the-art still struggle to produce, even using transformer architectures. In this paper, we analyze works in story generation that utilize machine learning approaches to (1) address story generation controllability, (2) incorporate commonsense knowledge, (3) infer reasonable character actions, and (4) generate creative language.
%R 10.18653/v1/2021.nuse-1.8
%U https://aclanthology.org/2021.nuse-1.8
%U https://doi.org/10.18653/v1/2021.nuse-1.8
%P 72-83
Markdown (Informal)
[Automatic Story Generation: Challenges and Attempts](https://aclanthology.org/2021.nuse-1.8) (Alabdulkarim et al., NUSE-WNU 2021)
ACL