Mining Legal Arguments in Court Decisions

TL;DR

This paper introduces a new legal argument annotation scheme, compiles a large annotated corpus of court decisions, and develops an argument mining model that surpasses existing legal NLP models, advancing legal argument analysis.

Contribution

It presents a novel annotation scheme rooted in legal theory, a large annotated corpus of ECHR decisions, and a superior argument mining model for legal texts.

Findings

The annotation scheme aligns with legal argumentation theory.

The corpus contains 373 court decisions with 15,000 annotated argument spans.

The proposed model outperforms state-of-the-art legal NLP argument mining models.

Abstract

Identifying, classifying, and analyzing arguments in legal discourse has been a prominent area of research since the inception of the argument mining field. However, there has been a major discrepancy between the way natural language processing (NLP) researchers model and annotate arguments in court decisions and the way legal experts understand and analyze legal argumentation. While computational approaches typically simplify arguments into generic premises and claims, arguments in legal research usually exhibit a rich typology that is important for gaining insights into the particular case and applications of law in general. We address this problem and make several substantial contributions to move the field forward. First, we design a new annotation scheme for legal arguments in proceedings of the European Court of Human Rights (ECHR) that is deeply rooted in the theory and practice of legal argumentation research. Second, we compile and annotate a large corpus of 373 court decisions (2.3M tokens and 15k annotated argument spans). Finally, we train an argument mining model that outperforms state-of-the-art models in the legal NLP domain and provide a thorough expert-based evaluation. All datasets and source codes are available under open lincenses at https://github.com/trusthlt/mining-legal-arguments.