Towards robustness of neural legal judgement system

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MTech (Res); 2023; Computer science and automation

Legal Judgment Prediction (LJP) implements Natural Language Processing (NLP) techniques to predict judgment results based on fact description. It can play a vital role as a legal assistant and benefit legal practitioners and regular citizens. Recently, the rapid advances in transformer- based pre-trained language models led to considerable improvement in this area. However, empirical results show that existing LJP systems are not robust to adversaries and noise. Also, they cannot handle large-length legal documents. In this work, we explore the robustness and efficiency of LJP systems even in a low data regime. In the first part, we empirically verify that existing state-of-the-art LJP systems are not robust. We further provide our novel architecture for LJP tasks which can handle extensive text lengths and adversarial examples. Our model performs better than state-of-the-art models, even in the presence of adversarial examples of the legal domain.