Recognizing vulnerability is crucial for understanding and implementing targeted support to empower individuals in need. This is especially important at the European Court of Human Rights (ECtHR), where the court adapts Convention standards to meet actual individual needs and thus ensures effective human rights protection. However, the concept of vulnerability remains elusive at the ECtHR and no prior NLP research has dealt with it. To enable future research in this area, we present VECHR, a novel expert-annotated multi-label dataset comprising of vulnerability type classification and explanation rationale. We benchmark the performance of state-of-the-art models on VECHR from both prediction and explainability perspectives. Our results demonstrate the challenging nature of the task with lower prediction performance and limited agreement between models and experts. Further, we analyze the robustness of these models in dealing with out-of-domain (OOD) data and observe overall limited performance. Our dataset poses unique challenges offering significant room for improvement regarding performance, explainability, and robustness.

翻译：识别脆弱性对于理解和实施针对性支持以赋权有需要的个体至关重要。这一点在欧洲人权法院（ECtHR）尤为关键，该法院通过调整《公约》标准以适应个体的实际需求，从而确保人权的有效保护。然而，脆弱性这一概念在欧洲人权法院中仍难以界定，且此前尚无自然语言处理研究涉足此领域。为促进该领域的未来研究，我们提出了VECHR——一个由专家标注的新型多标签数据集，包含脆弱性类型分类及其解释依据。我们从预测与可解释性两个维度，对现有最优模型在VECHR上的性能进行了基准测试。结果表明，该任务具有挑战性：预测性能较低，且模型与专家之间一致性有限。此外，我们分析了这些模型在处理域外数据时的鲁棒性，观察到其整体表现有限。本数据集带来了独特挑战，为提升性能、可解释性及鲁棒性提供了显著的改进空间。