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UID:53f7c773d9311c84025bf478063ac90e-469
DTSTAMP:19700101T120011Z
DESCRIPTION:Towards Robustness of Neural Legal Judgment System
URL;VALUE=URI:https://www.csa.iisc.ac.in/newweb/event/469/towards-robustness-of-neural-legal-judgment-system/
SUMMARY: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 benefits legal practitioners and regular citizens. Recently, the rapid advances of transformer-based pretrained 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 verified 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.

In the second part, we investigate the approach for the LJP system in a low data regime. We provide a novel architecture using a few-shot approach that is also robust to adversaries. We conducted extensive experiments on American, European, and Indian legal datasets in the few-shot scenario. Our model, though trained using the few-shot approach, performs as well as state-of-the-art models which are trained using large datasets in the legal domain.
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