On What it Means to Pay Your Fair Share: Towards Automatically Mapping Different Conceptions of Tax Justice in Legal Research Literature
Reto Gubelmann, Peter Hongler, Elina Margadant, Siegfried Handschuh
Abstract
In this article, we explore the potential and challenges of applying transformer-based pre-trained language models (PLMs) and statistical methods to a particularly challenging, yet highly important and largely uncharted domain: normative discussions in tax law research. On our conviction, the role of NLP in this essentially contested territory is to make explicit implicit normative assumptions, and to foster debates across ideological divides. To this goal, we propose the first steps towards a method that automatically labels normative statements in tax law research, and that suggests the normative background of these statements. Our results are encouraging, but it is clear that there is still room for improvement.- Anthology ID:
- 2022.nllp-1.2
- Volume:
- Proceedings of the Natural Legal Language Processing Workshop 2022
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates (Hybrid)
- Editors:
- Nikolaos Aletras, Ilias Chalkidis, Leslie Barrett, Cătălina Goanță, Daniel Preoțiuc-Pietro
- Venue:
- NLLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 12–30
- Language:
- URL:
- https://aclanthology.org/2022.nllp-1.2
- DOI:
- 10.18653/v1/2022.nllp-1.2
- Cite (ACL):
- Reto Gubelmann, Peter Hongler, Elina Margadant, and Siegfried Handschuh. 2022. On What it Means to Pay Your Fair Share: Towards Automatically Mapping Different Conceptions of Tax Justice in Legal Research Literature. In Proceedings of the Natural Legal Language Processing Workshop 2022, pages 12–30, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- On What it Means to Pay Your Fair Share: Towards Automatically Mapping Different Conceptions of Tax Justice in Legal Research Literature (Gubelmann et al., NLLP 2022)
- PDF:
- https://preview.aclanthology.org/emnlp22-frontmatter/2022.nllp-1.2.pdf