Abstract
Predicting how Congressional legislators will vote is important for understanding their past and future behavior. However, previous work on roll-call prediction has been limited to single session settings, thus not allowing for generalization across sessions. In this paper, we show that text alone is insufficient for modeling voting outcomes in new contexts, as session changes lead to changes in the underlying data generation process. We propose a novel neural method for encoding documents alongside additional metadata, achieving an average of a 4% boost in accuracy over the previous state-of-the-art.- Anthology ID:
- P18-2081
- Volume:
- Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Editors:
- Iryna Gurevych, Yusuke Miyao
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 510–515
- Language:
- URL:
- https://aclanthology.org/P18-2081
- DOI:
- 10.18653/v1/P18-2081
- Cite (ACL):
- Anastassia Kornilova, Daniel Argyle, and Vladimir Eidelman. 2018. Party Matters: Enhancing Legislative Embeddings with Author Attributes for Vote Prediction. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 510–515, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- Party Matters: Enhancing Legislative Embeddings with Author Attributes for Vote Prediction (Kornilova et al., ACL 2018)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/P18-2081.pdf