发表机构
School of EECS, The University of Queensland（昆士兰大学电气、电子与计算机工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对法律案例检索中现有方法推理耗时的问题，提出Cassette框架，通过排序和特征匹配蒸馏将重型教师模型知识迁移至轻量学生双编码器，在保持性能的同时大幅提升检索效率。
AI 中文摘要
法律案例检索（LCR）是一种重要工具，不仅帮助法律从业者高效检索先例，也使普通民众无需依赖昂贵的专业法律服务即可找到有价值的法律案例信息。我们先前的工作CaseLink证明了利用案例到案例的图结构来提高检索准确性的有效性。然而，在大规模法律数据库上进行推理时，其高计算成本限制了其在实际场景中的应用。主要的低效之处在于构建测试时图以及计算案例间成对词频相似度。对于n个法律案例，该过程具有O(n^2)复杂度，随着候选案例数量的增加，运行时间变得难以承受。例如，在包含1,563个候选案例的数据库（COLIEE2022）上，单个查询的检索时间超过500毫秒，而在包含55,192个候选案例的数据库（LeCaRDv2）上，运行时间将急剧增加至超过3,500秒。为了在实现显著加速的同时进一步提升检索性能，在本扩展论文中，提出了Cassette框架，采用包含排序目标和特征匹配目标的蒸馏策略，将知识从强大且训练良好的重型教师检索器有效迁移到轻量且高效的混合学生双编码器。具体而言，学生查询编码器实现为多层感知机模型，用于快速在线处理，而学生候选编码器采用图神经网络架构，适用于案例数据库内的离线方式。在三个基准数据集上进行了广泛实验，结果验证了排序蒸馏的有效性，同时实现了高效率。代码已发布在https URL上。
英文摘要
Legal case retrieval (LCR) is an essential tool for not only assisting legal practitioners to efficiently retrieve precedents but also enabling ordinary individuals to find valuable legal case information without relying on expensive professional legal services. Our previous work CaseLink demonstrated the effectiveness of using case to case graph structures to improve retrieval accuracy. However, its high computational cost during inference on large-scale legal databases limits its practical use in real-world settings. The main inefficiency comes from constructing test time graphs and computing pairwise term frequency similarities of cases. This process has O(n^2) complexity for n legal cases, making the runtime prohibitive as the number of candidates grows. For example, the retrieval time for one query on a database (COLIEE2022) with 1,563 candidate cases is more than 500 milliseconds, while the runtime would increase drastically to more than 3,500 seconds for a database (LeCaRDv2) with 55,192 candidate cases. To further enhance the retrieval performance while achieving a significant speed-up, in this extension paper, Cassette framework is proposed with a distillation strategy involving ranking objective and eigen-matching objective for an effective transfer of knowledge from a powerful and well-trained heavy teacher retriever to a lightweight and efficient hybrid student dual encoder. Specifically, the student query encoder is implemented as a multilayer perceptron model designed for fast online processing, whereas the student candidate encoder adopts a GNN architecture, suitable for an offline manner within the case database. Extensive experiments are conducted on three benchmark datasets, and the results verify the effectiveness of the ranking distillation while achieving high efficiency. The code has been released on https://github.com/yanran-tang/Cassette/.
