Abstract
Automatically generating stories is a challenging problem that requires producing causally related and logical sequences of events about a topic. Previous approaches in this domain have focused largely on one-shot generation, where a language model outputs a complete story based on limited initial input from a user. Here, we instead focus on the task of interactive story generation, where the user provides the model mid-level sentence abstractions in the form of cue phrases during the generation process. This provides an interface for human users to guide the story generation. We present two content-inducing approaches to effectively incorporate this additional information. Experimental results from both automatic and human evaluations show that these methods produce more topically coherent and personalized stories compared to baseline methods.- Anthology ID:
- 2020.aacl-main.59
- Volume:
- Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing
- Month:
- December
- Year:
- 2020
- Address:
- Suzhou, China
- Editors:
- Kam-Fai Wong, Kevin Knight, Hua Wu
- Venue:
- AACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 588–597
- Language:
- URL:
- https://aclanthology.org/2020.aacl-main.59
- DOI:
- Cite (ACL):
- Faeze Brahman, Alexandru Petrusca, and Snigdha Chaturvedi. 2020. Cue Me In: Content-Inducing Approaches to Interactive Story Generation. In Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing, pages 588–597, Suzhou, China. Association for Computational Linguistics.
- Cite (Informal):
- Cue Me In: Content-Inducing Approaches to Interactive Story Generation (Brahman et al., AACL 2020)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2020.aacl-main.59.pdf