sxu/VECHR

---
license: afl-3.0
language:
- en
tags:
- legal
size_categories:
- n<1K
---

# Dataset Card for VECHR
### Dataset Summary
[VECHR: A Dataset for Explainable and Robust Classification of Vulnerability Type in the European Court of Human Rights](https://aclanthology.org/2023.emnlp-main.718/)

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 to ensure 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 perspective. Our results demonstrate the challenging nature of 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 a significant room for improvement regarding performance, explainability and robustness.

### Languages
English

# Citation Information

@inproceedings{xu-etal-2023-vechr,
title = "{VECHR}: A Dataset for Explainable and Robust Classification of Vulnerability Type in the {E}uropean Court of Human Rights",
author = "Xu, Shanshan and
Staufer, Leon and
T.y.s.s, Santosh and
Ichim, Oana and
Heri, Corina and
Grabmair, Matthias",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.718",
doi = "10.18653/v1/2023.emnlp-main.718",
pages = "11738--11752",
}

原始信息汇总

数据集卡片 VECHR

数据集概述

VECHR 是一个用于欧洲人权法院中脆弱性类型分类和解释理由的新颖专家标注多标签数据集。该数据集旨在支持未来在这一领域的研究，并从预测和可解释性的角度对最先进的模型进行了性能基准测试。结果显示，该任务具有挑战性，预测性能较低，模型与专家之间的一致性有限。此外，我们还分析了这些模型处理域外（OOD）数据的鲁棒性，并观察到整体性能有限。VECHR 数据集提出了独特的挑战，为性能、可解释性和鲁棒性的改进提供了显著空间。

语言

英语

引用信息

@inproceedings{xu-etal-2023-vechr, title = "{VECHR}: A Dataset for Explainable and Robust Classification of Vulnerability Type in the {E}uropean Court of Human Rights", author = "Xu, Shanshan and Staufer, Leon and T.y.s.s, Santosh and Ichim, Oana and Heri, Corina and Grabmair, Matthias", booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2023", address = "Singapore", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.emnlp-main.718", doi = "10.18653/v1/2023.emnlp-main.718", pages = "11738--11752", }