Towards Controllable Story Generation

Nanyun Peng, Marjan Ghazvininejad, Jonathan May, Kevin Knight


Abstract
We present a general framework of analyzing existing story corpora to generate controllable and creative new stories. The proposed framework needs little manual annotation to achieve controllable story generation. It creates a new interface for humans to interact with computers to generate personalized stories. We apply the framework to build recurrent neural network (RNN)-based generation models to control story ending valence and storyline. Experiments show that our methods successfully achieve the control and enhance the coherence of stories through introducing storylines. with additional control factors, the generation model gets lower perplexity, and yields more coherent stories that are faithful to the control factors according to human evaluation.
Anthology ID:
W18-1505
Volume:
Proceedings of the First Workshop on Storytelling
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venues:
NAACL | Story-NLP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
43–49
Language:
URL:
https://aclanthology.org/W18-1505
DOI:
10.18653/v1/W18-1505
Bibkey:
Cite (ACL):
Nanyun Peng, Marjan Ghazvininejad, Jonathan May, and Kevin Knight. 2018. Towards Controllable Story Generation. In Proceedings of the First Workshop on Storytelling, pages 43–49, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
Towards Controllable Story Generation (Peng et al., 2018)
Copy Citation:
PDF:
https://aclanthology.org/W18-1505.pdf
Data
ROCStories
Terminologies: