Classifying proportionality - identification of a legal argument

Abstract

AbstractProportionality is a central and globally spread argumentation technique in public law. This article provides a conceptual introduction to proportionality and argues that such a domain-specific form of argumentation is particularly interesting for argument mining. As a major contribution of this article, we share a new dataset for which proportionality has been annotated. The dataset consists of 300 German Federal Constitutional Court decisions annotated at the sentence level (54,929 sentences). In addition to separating textual parts, a fine-grained system of proportionality categories was used. Finally, we used these data for a classification task. We built classifiers that predict whether or not proportionality is invoked in a sentence. We employed several models, including neural and deep learning models and transformers. A BERT-BiLSTM-CRF model performed best.