Abstract:
Over the past few years, there has been an increase in interest in predicting court
decisions. On the one hand, as society has continued to expand, numerous social ties
and social contradictions have grown more complex, and the number of court cases has
rapidly increased, adding to the difficulty of convicting and sentencing case management.
To get a thorough judgement basis, judges and pertinent case-handling staff generally
need to manually review a substantial number of materials and legal documents. This
technique requires a lot of time and labor, and it is not very productive. An automated
system that could assist a judge in predicting the outcome of a case would help expedite
the judicial process. For such a system to be practically useful, predictions by the system
should be explainable. To promote research in developing such a system, we introduce
LJPE (Legal Judgment Prediction and Explanation) Dataset for the Pakistan Legal
Documents. LJPE is a large corpus of 11k Pakistan Supreme Court cases annotated
with original court decisions. A portion of the corpus (a separate test set) is annotated
with gold standard explanations by legal experts. We experiment with a battery of
baseline models for case predictions and propose a hierarchical occlusion-based model
for explainability.
