Transformer-based architecture for judgment prediction and explanation in legal proceedings

Author Identifier

Arooba Maqsood: https://orcid.org/0009-0001-0853-9268

Document Type

Conference Proceeding

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume

14994 LNCS

First Page

20

Last Page

36

Publisher

Springer

School

School of Science

RAS ID

76284

Comments

Maqsood, A., Ul-Hasan, A., & Shafait, F. (2024, August). Transformer-based architecture for judgment prediction and explanation in legal proceedings. In International Workshop on Document Analysis Systems (pp. 20-36). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-70442-0_2

Abstract

Advancements in language understanding have helped researchers develop a verdict prediction system that can assist a court judge in verdict formulation. This technological intervention can help streamline and standardize the decision-making process across all levels of courts. One key benefit of developing such a system is that the junior judges can benefit from the collective knowledge stored in the knowledge base, improving their ability to make consistent and well-informed decisions. For any such system to be practically useful, predictions should be explainable too. This research proposes a hierarchical pipeline that aims to leverage domain-specific variants of BERT to enhance the process of informed decision-making. The research is mainly divided into two modules: ‘Legal Judgment Prediction (LJP)’ and ‘Legal Judgment Explanation Extraction (LJEE)’. The LJP task pertains to predicting the outcome of legal decisions concerning the appellant. In contrast, the LJEE refers to extracting out the phrases/clauses that led to the final decision. To promote research in developing such a system for Pakistani legal documents, this paper also introduces the VerdictVaultPK dataset. The dataset comprises around 11,943 rental-property case proceedings, each annotated with the court decisions indicating whether the appeal was allowed or dismissed. This research highlights how the use of domain-specific transformer models enriches semantic embeddings, contributing to a substantial accuracy improvement of 3–4%.

DOI

10.1007/978-3-031-70442-0_2

Access Rights

subscription content

Link to Full Text

Share

 
COinS