TY  - BOOK
AU  - Raj, Rohit
AU  - Susheela Devi, V advised
TI  - Towards robustness of neural legal judgement system
U1  - 600 
PY  - 2023///
CY  - Bangalore
PB  - IISc
KW  - Legal Judgment Prediction
KW  - Natural Language Processing
KW  - Legal dataset
N1  - include bibliographic reference and index; MTech (Res); 2023; Computer science and automation
N2  - 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
UR  - https://etd.iisc.ac.in/handle/2005/6145
ER  -