Adversarially Robust Neural Legal Judgement Systems

TL;DR

This paper introduces a novel approach to enhance the robustness of legal judgment prediction systems against adversarial attacks, demonstrating significant improvements across multiple datasets.

Contribution

It is the first to focus on increasing the adversarial robustness of existing legal judgment prediction systems, addressing a critical gap in the field.

Findings

Existing LJP systems are vulnerable to adversarial attacks.

The proposed method significantly improves robustness against attacks.

Experiments on three datasets validate the effectiveness of the approach.

Abstract

Legal judgment prediction is the task of predicting the outcome of court cases on a given text description of facts of cases. These tasks apply Natural Language Processing (NLP) techniques to predict legal judgment results based on facts. Recently, large-scale public datasets and NLP models have increased research in areas related to legal judgment prediction systems. For such systems to be practically helpful, they should be robust from adversarial attacks. Previous works mainly focus on making a neural legal judgement system; however, significantly less or no attention has been given to creating a robust Legal Judgement Prediction(LJP) system. We implemented adversarial attacks on early existing LJP systems and found that none of them could handle attacks. In this work, we proposed an approach for making robust LJP systems. Extensive experiments on three legal datasets show significant improvements in our approach over the state-of-the-art LJP system in handling adversarial attacks. To the best of our knowledge, we are the first to increase the robustness of early-existing LJP systems.