Automatic Story Generation: Challenges and Attempts

Amal Alabdulkarim, Siyan Li, Xiangyu Peng


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.
Anthology ID:
2021.nuse-1.8
Volume:
Proceedings of the Third Workshop on Narrative Understanding
Month:
June
Year:
2021
Address:
Virtual
Venues:
NAACL | NUSE
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
72–83
Language:
URL:
https://aclanthology.org/2021.nuse-1.8
DOI:
10.18653/v1/2021.nuse-1.8
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.nuse-1.8.pdf
Data
ATOMICCommonsenseQAConceptNetGLUCOSESocial IQAWritingPrompts