2021/08/25 by Reto Gubelmann, Gubelmann, Reto, Peter Hongler +3 · 2 citations
Computer Science · Engineering · Social Sciences · #Artificial Intelligence in Law #Computation and Language (cs.CL) #Computer science #Computers and Society (cs.CY) #Electrical engineering #Engineering #FOS: Computer and information sciences #Law #Law and economics #Multi-Agent Systems and Negotiation #Normative #Political science #Representation (politics) #Sociology #Topic Modeling #Transformer #Voltage #cs.CL #cs.CY
paper · pdf · doi:10.48550/arxiv.2108.11215
published in arXiv (Cornell University) (Cornell University) · 11 pages, 3 figures
arxiv created 2021/08/25 · openalex publication_date 2021/08/25 · arxiv updated 2021/08/26 · openalex created_date 2021/08/30 · openalex updated_date 2026/07/28
In this article, we explore the potential of transformer-based language models (LMs) to correctly represent normative statements in the legal domain, taking tax law as our use case. In our experiment, we use a variety of LMs as bases for both word- and sentence-based clusterers that are then evaluated on a small, expert-compiled test-set, consisting of real-world samples from tax law research literature that can be clearly assigned to one of four normative theories. The results of the experiment show that clusterers based on sentence-BERT-embeddings deliver the most promising results. Based on this main experiment, we make first attempts at using the best performing models in a bootstrapping loop to build classifiers that map normative claims on one of these four normative theories.