Mining Legal Arguments in Court Decisions
This work addresses the problem of improving argument mining for legal experts by bridging the gap between computational simplification and rich legal typology, though it is incremental as it builds on existing field research.
The paper tackled the discrepancy between NLP and legal expert approaches to argument mining in court decisions by designing a new annotation scheme rooted in legal theory, compiling a large annotated corpus of 373 decisions (2.3M tokens, 15k spans), and training a model that outperforms state-of-the-art models in legal NLP.
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.