AIDA: Legal Judgment Predictions for Non-Professional Fact Descriptions via Partial-and-Imbalanced Domain Adaptation

TL;DR

This paper introduces AIDA, a novel deep learning approach for partial-and-imbalanced domain adaptation in legal judgment prediction, effectively leveraging related class data to improve accuracy on non-professional fact descriptions.

Contribution

The paper proposes a new partial imbalanced domain adaptation technique (AIDA) that utilizes hierarchy weighting to enhance legal judgment predictions from non-professional descriptions.

Findings

AIDA outperforms existing algorithms in legal judgment prediction tasks.

The hierarchy weighting adaptation effectively handles non-shared class data.

Experimental results demonstrate significant accuracy improvements.

Abstract

In this paper, we study the problem of legal domain adaptation problem from an imbalanced source domain to a partial target domain. The task aims to improve legal judgment predictions for non-professional fact descriptions. We formulate this task as a partial-and-imbalanced domain adaptation problem. Though deep domain adaptation has achieved cutting-edge performance in many unsupervised domain adaptation tasks. However, due to the negative transfer of samples in non-shared classes, it is hard for current domain adaptation model to solve the partial-and-imbalanced transfer problem. In this work, we explore large-scale non-shared but related classes data in the source domain with a hierarchy weighting adaptation to tackle this limitation. We propose to embed a novel pArtial Imbalanced Domain Adaptation technique (AIDA) in the deep learning model, which can jointly borrow sibling knowledge from non-shared classes to shared classes in the source domain and further transfer the shared classes knowledge from the source domain to the target domain. Experimental results show that our model outperforms the state-of-the-art algorithms.