【摘要】 研究人员曾致力于通过案件匹配的方法找到相似的案件，但案件匹配的方法依赖于文本相似性，文本相似并不等同于案件相似；而且案件匹配的方法普遍缺乏解释性。为了克服案件匹配的缺点，定义了一个新问题，即案情要素关联与证据提取。该问题旨在基于案情要素而非文本相似性来预测关联结果，并提取关键事实细节作为证据以解释关联结果，这一新问题更符合法律从业者的实际需求。为了使所提出的模型在这一新问题上表现更好，引入了对比学习，以解决模型在获取文本表征时过度依赖案情要素直接表达的问题，从而使注意力权重均衡分布在相同案情要素的不同表达上，进而提升模型效果。在公开数据集和自建数据集上进行了实验。实验结果表明，与文本匹配模型相比，所提模型在accuracy和precision上均提高了约20%,在recall和F1上均提高了约30%。更多还原

【Abstract】 Researchers in the past have devoted themselves to finding similar cases through the method of case matching.But the case-matching method depends on the text similarity.Similarity of texts is not equal to similarity of cases.Moreover, case ma-tching lacks interpretability.To address the shortcomings of case matching, we define a new problem, case element association with evidence extraction, which aims to predict the association results by elements rather than text similarity, and extracts factual details as evidence to explain the association result.This new problem is more in line with the actual needs of legal practitioners.In order to make the proposed model perform better on this new problem, contrastive learning is introduced to solve the problem of over-dependence on direct expressions of elements, which makes the attention weights evenly distributed on different expressions of same elements, thereby improving the effect of our model.We perform experiments on public and self-constructed datasets.Experiment results show that compared with text matching models, the proposed model improves the accuracy and precision by about 20%,and improves the recall and F1 by about 30%.更多还原