V. Susheela Devi

Rohit Raj, V Susheela Devi

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.

Govind Sharma, Swyam Prakash Singh, V. Susheela Devi et al.

Usual relations between entities could be captured using graphs; but those of a higher-order -- more so between two different types of entities (which we term "left" and "right") -- calls for a "bipartite hypergraph". For example, given a left set of symptoms and right set of diseases, the relation between a set subset of symptoms (that a patient experiences at a given point of time) and a subset of diseases (that he/she might be diagnosed with) could be well-represented using a bipartite hyperedge. The state-of-the-art in embedding nodes of a hypergraph is based on learning the self-attention structure between node-pairs from a hyperedge. In the present work, given a bipartite hypergraph, we aim at capturing relations between node pairs from the cross-product between the left and right hyperedges, and term it a "cross-attention" (CAT) based model. More precisely, we pose "bipartite hyperedge link prediction" as a set-matching (SETMAT) problem and propose a novel neural network architecture called CATSETMAT for the same. We perform extensive experiments on multiple bipartite hypergraph datasets to show the superior performance of CATSETMAT, which we compare with multiple techniques from the state-of-the-art. Our results also elucidate information flow in self- and cross-attention scenarios.