Exploiting Contrastive Learning and Numerical Evidence for Improving Confusing Legal Judgment Prediction. (arXiv:2211.08238v1 [cs.CL])

Given the fact description text of a legal case, legal judgment prediction
(LJP) aims to predict the case's charge, law article and penalty term. A core
problem of LJP is how to distinguish confusing legal cases, where only subtle
text differences exist. Previous studies fail to distinguish different
classification errors with a standard cross-entropy classification loss, and
ignore the numbers in the fact description for predicting the term of penalty.
To tackle these issues, in this work, first, we propose …

arxiv evidence judgment legal numerical prediction