Abstract
The ability to change a person’s mind on a given issue depends both on the arguments they are presented with and on their underlying perspectives and biases on that issue. Predicting stance changes require characterizing both aspects and the interaction between them, especially in realistic settings in which stance changes are very rare. In this paper, we suggest a modular learning approach, which decomposes the task into multiple modules, focusing on different aspects of the interaction between users, their beliefs, and the arguments they are exposed to. Our experiments show that our modular approach archives significantly better results compared to the end-to-end approach using BERT over the same inputs.- Anthology ID:
- 2020.coling-main.35
- Volume:
- Proceedings of the 28th International Conference on Computational Linguistics
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona, Spain (Online)
- Editors:
- Donia Scott, Nuria Bel, Chengqing Zong
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 396–406
- Language:
- URL:
- https://aclanthology.org/2020.coling-main.35
- DOI:
- 10.18653/v1/2020.coling-main.35
- Cite (ACL):
- Aldo Porco and Dan Goldwasser. 2020. Predicting Stance Change Using Modular Architectures. In Proceedings of the 28th International Conference on Computational Linguistics, pages 396–406, Barcelona, Spain (Online). International Committee on Computational Linguistics.
- Cite (Informal):
- Predicting Stance Change Using Modular Architectures (Porco & Goldwasser, COLING 2020)
- PDF:
- https://preview.aclanthology.org/ingest-bitext-workshop/2020.coling-main.35.pdf