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能弱势个体至关重要。这一工作在欧盟人权法院（ECtHR）尤为关键——该法院通过调整《公约》标准以适应个体实际需求，从而保障人权的有效实现。然而，脆弱性概念在ECtHR中仍缺乏明确界定，且现有自然语言处理研究尚未涉及该议题。为促进该领域的未来研究，我们提出VECHR——一个由专家标注的新型多标签数据集，涵盖脆弱性类型分类及解释依据。我们从预测性能与可解释性两个维度基准测试了多项先进模型在VECHR上的表现。结果表明该任务具有挑战性：模型预测性能较低，且模型与专家标注之间的共识有限。此外，我们分析了这些模型处理域外数据时的鲁棒性，观察到其整体性能受限。本研究提出的数据集在性能、可解释性与鲁棒性方面具有独特的挑战性，为后续研究提供了显著的改进空间。