Knowledge is Power: Understanding Causality Makes Legal judgment Prediction Models More Generalizable and Robust

Abstract

Legal judgment Prediction (LJP), aiming to predict a judgment based on fact descriptions, serves as legal assistance to mitigate the great work burden of limited legal practitioners. Most existing methods apply various large-scale pre-trained language models (PLMs) finetuned in LJP tasks to obtain consistent improvements. However, we discover the fact that the state-of-the-art (SOTA) model makes judgment predictions according to wrong (or non-casual) information, which not only weakens the model's generalization capability but also results in severe social problems like discrimination. Here, we analyze the causal mechanism misleading the LJP model to learn the spurious correlations, and then propose a framework to guide the model to learn the underlying causality knowledge in the legal texts. Specifically, we first perform open information extraction (OIE) to refine the text having a high proportion of causal information, according to which we generate a new set of data. Then, we design a model learning the weights of the refined data and the raw data for LJP model training. The extensive experimental results show that our model is more generalizable and robust than the baselines and achieves a new SOTA performance on two commonly used legal-specific datasets.

Predicting Critical Path of Labor Dispute Resolution in Legal Domain by Machine Learning Models Based on SHapley Additive exPlanations and Soft Voting Strategy

“…The introduction of Artificial Intelligence (AI) techniques has brought new opportunities to the legal domain. It can help legal professionals escape from repetitive tasks (e.g., legal judgment prediction [3][4][5], legal question answering [6][7][8], and legal case retrieval [9][10][11]) and then have time to focus on more valuable things. Dispute resolution, being a crucial component of the legal domain, has obtained considerable attention from researchers regarding how artificial intelligence can be utilized to address issues related to disputes, such as predicting dispute occurrences, dispute resolution methods, and dispute resolution outcomes.…”