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In this article, we explore the potential of transformer-based languagemodels (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 asbases for both word- and sentence-based clusterers that are then evaluated on asmall, expert-compiled test-set, consisting of real-world samples from tax lawresearch literature that can be clearly assigned to one of four normativetheories. The results of the experiment show that clusterers based onsentence-BERT-embeddings deliver the most promising results. Based on this mainexperiment, we make first attempts at using the best performing models in abootstrapping loop to build classifiers that map normative claims on one ofthese four normative theories.

Exploring the Promises of Transformer-Based LMs for the Representation of Normative Claims in the Legal Domain