SLJP: Semantic Extraction based Legal Judgment Prediction

TL;DR

This paper introduces SLJP, a semantic extraction-based model for legal judgment prediction that leverages pretrained transformers to understand complex legal documents and improve prediction accuracy in Indian courts.

Contribution

The paper presents a novel semantic extraction approach using pretrained transformers and a divide-and-conquer strategy for improved legal judgment prediction in Indian legal systems.

Findings

Achieved promising results on ILDC and ILSI datasets.

Demonstrated higher performance and less degradation over epochs.

Utilized attention mechanisms for effective judgment prediction.

Abstract

Legal Judgment Prediction (LJP) is a judicial assistance system that recommends the legal components such as applicable statues, prison term and penalty term by analyzing the given input case document. Indian legal system is in the need of technical assistance such as artificial intelligence to solve the crores of pending cases in various courts for years and its being increased day to day. Most of the existing Indian models did not adequately concentrate on the semantics embedded in the fact description (FD) that impacts the decision. The proposed semantic extraction based LJP (SLJP) model provides the advantages of pretrained transformers for complex unstructured legal case document understanding and to generate embeddings. The model draws the in-depth semantics of the given FD at multiple levels i.e., chunk and case document level by following the divide and conquer approach. It creates the concise view of the given fact description using the extracted semantics as per the original court case document structure and predicts judgment using attention mechanism. We tested the model performance on two available Indian datasets Indian Legal Documents corpus (ILDC) and Indian Legal Statue Identification (ILSI) and got promising results. Also shown the highest performance and less performance degradation for increased epochs than base models on ILDC dataset.