Publication details

G. Marino, D. Licari, P. Bushipaka, G. Comandé and T. Cucinotta. "Automatic Rhetorical Roles Classification for Legal Documents using LEGAL-TransformerOverBERT," in Proceedings of the International Workshop on Automated Semantic Analysis of Information in Legal Text (ASAIL 2023), co-located with the 19th International Conference on Artificial Intelligence and Law (ICAIL 2023), CEUR Workshop Proceedings, Vol. 3441, pp. 28-36, June 19-23, 2023, Braga, Portugal.

Abstract

Automatic identification of rhetorical roles can help in many downstream applications of legal documents analysis, such as legal decisions summarization and legal search. This is usually a complex task, even for humans, due to its inherent subjectivity and to the difficulty of capturing sentence context in very long legal documents. We propose a novel approach, based on Hierarchical Transformers, which overcomes these problems and achieves promising results on two different datasets of Italian and English legal judgments. Specifically, we introduce LEGAL-TransformerOverBERT (LEGAL-ToBERT), a model based on the stacking of a transformer encoder over a legal-domain-specific BERT model, and show that our approach is able to significantly improve the baselines set by the stand-alone LEGAL-BERT models, by capturing the relationships between different sentences of the same document. We make our models available and ready-to-use for downstream applications of rhetorical roles classification in the legal context both for the Italian and English language.

License: Creative Commons License Attribution 4.0 (CC-BY 4.0)

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Last updated on
07 November 2024