Legal case retrieval, which aims to retrieve relevant cases given a query
case, plays an essential role in the legal system. While recent research
efforts improve the performance of traditional ad-hoc retrieval models, legal
case retrieval is still challenging since queries are legal cases, which
contain hundreds of tokens. Legal cases are much longer and more complicated
than keywords queries. Apart from that, the definition of legal relevance is
beyond the general definition. In addition to general topical relevance, the
relevant cases also involve similar situations and legal elements, which can
support the judgment of the current case. In this paper, we propose an
interaction-focused network for legal case retrieval with a multi-view
contrastive learning objective. The contrastive learning views, including
case-view and element-view, aim to overcome the above challenges. The case-view
contrastive learning minimizes the hidden space distance between relevant legal
case representations produced by a pre-trained language model (PLM) encoder.
The element-view builds positive and negative instances by changing legal
elements of cases to help the network better compute legal relevance. To
achieve this, we employ a legal element knowledge-aware indicator to detect
legal elements of cases. We conduct extensive experiments on the benchmark of
relevant case retrieval. Evaluation results indicate our proposed method
obtains significant improvement over the existing methods.

提出了一种基于交互式多视图对比学习目标的法律案例检索网络，其中对比学习的视图包括案例视图和元素视图，以最小化由预训练语言模型编码器生成的相关法律案例表示之间的隐藏空间距离，并通过改变案例的法律元素来建立正负实例以帮助网络更好地计算法律相关性，实验证明其在相关案例检索基准上获得了显着的改进。