Abstract
In this paper, we introduce the task of modeling interpersonal relationships for story generation. For addressing this task, we propose Relationships as Latent Variables for Story Generation, (ReLiSt). ReLiSt generates stories sentence by sentence and has two major components - a relationship selector and a story continuer. The relationship selector specifies a latent variable to pick the relationship to exhibit in the next sentence and the story continuer generates the next sentence while expressing the selected relationship in a coherent way. Our automatic and human evaluations demonstrate that ReLiSt is able to generate stories with relationships that are more faithful to desired relationships while maintaining the content quality. The relationship assignments to sentences during inference brings interpretability to ReLiSt.- Anthology ID:
- 2022.emnlp-main.613
- Volume:
- Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Editors:
- Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 8970–8987
- Language:
- URL:
- https://aclanthology.org/2022.emnlp-main.613
- DOI:
- 10.18653/v1/2022.emnlp-main.613
- Cite (ACL):
- Anvesh Rao Vijjini, Faeze Brahman, and Snigdha Chaturvedi. 2022. Towards Inter-character Relationship-driven Story Generation. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 8970–8987, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- Towards Inter-character Relationship-driven Story Generation (Vijjini et al., EMNLP 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2022.emnlp-main.613.pdf