Leveraging Task Dependency and Contrastive Learning for Case Outcome Classification on European Court of Human Rights Cases

TL;DR

This paper introduces a novel approach for classifying case outcomes in European Court of Human Rights cases by combining task dependency analysis with hierarchical contrastive learning, resulting in improved classification accuracy.

Contribution

The study proposes a new hierarchical contrastive loss method that enhances case representation clustering based on articles and outcomes, improving classification performance.

Findings

Model achieves consistent performance improvements over baseline methods.

Hierarchical contrastive loss effectively clusters cases by article and outcome.

Task dependency analysis informs better feature representations.

Abstract

We report on an experiment in case outcome classification on European Court of Human Rights cases where our model first learns to identify the convention articles allegedly violated by the state from case facts descriptions, and subsequently uses that information to classify whether the court finds a violation of those articles. We assess the dependency between these two tasks at the feature and outcome level. Furthermore, we leverage a hierarchical contrastive loss to pull together article-specific representations of cases at the higher level, leading to distinctive article clusters. The cases in each article cluster are further pulled closer based on their outcome, leading to sub-clusters of cases with similar outcomes. Our experiment results demonstrate that, given a static pre-trained encoder, our models produce a small but consistent improvement in classification performance over single-task and joint models without contrastive loss.