论文标题
对欧洲人权法院案件的法律判决预测，以更好地与专家保持一致
Deconfounding Legal Judgment Prediction for European Court of Human Rights Cases Towards Better Alignment with Experts
论文作者
论文摘要
这项工作表明，没有专家信息调整的法律判断预测系统可能很容易受到浅层的影响，分散了由语料库结构，案例分布和混杂因素引起的表面信号。为了减轻这种情况，我们使用域专业知识来战略性地识别统计学上的预测性但在法律上无关紧要的信息。我们采用对抗性训练来防止系统依靠它。我们通过采用可解释性技术并与专家注释进行比较来评估我们的变形模型。定量实验和定性分析表明，与仅培训预测的基准相比，我们的脱浓模型与专家理由的一致性一致地与专家理由保持更好。我们进一步为欧洲人权法院现有基准数据集的验证和测试分区提供了一组参考专家注释。
This work demonstrates that Legal Judgement Prediction systems without expert-informed adjustments can be vulnerable to shallow, distracting surface signals that arise from corpus construction, case distribution, and confounding factors. To mitigate this, we use domain expertise to strategically identify statistically predictive but legally irrelevant information. We adopt adversarial training to prevent the system from relying on it. We evaluate our deconfounded models by employing interpretability techniques and comparing to expert annotations. Quantitative experiments and qualitative analysis show that our deconfounded model consistently aligns better with expert rationales than baselines trained for prediction only. We further contribute a set of reference expert annotations to the validation and testing partitions of an existing benchmark dataset of European Court of Human Rights cases.
