Recent efforts have aimed to improve AI machines in legal case matching by integrating legal domain knowledge. However, successful legal case matching requires the tacit knowledge of legal practitioners, which is difficult to verbalize and encode into machines. This emphasizes the crucial role of involving legal practitioners in high-stakes legal case matching. To address this, we propose a collaborative matching framework called Co-Matching, which encourages both the machine and the legal practitioner to participate in the matching process, integrating tacit knowledge. Unlike existing methods that rely solely on the machine, Co-Matching allows both the legal practitioner and the machine to determine key sentences and then combine them probabilistically. Co-Matching introduces a method called ProtoEM to estimate human decision uncertainty, facilitating the probabilistic combination. Experimental results demonstrate that Co-Matching consistently outperforms existing legal case matching methods, delivering significant performance improvements over human- and machine-based matching in isolation (on average, +5.51% and +8.71%, respectively). Further analysis shows that Co-Matching also ensures better human-machine collaboration effectiveness. Our study represents a pioneering effort in human-machine collaboration for the matching task, marking a milestone for future collaborative matching studies.

翻译：近年来，研究致力于通过整合法律领域知识来提升AI机器在法律案例匹配中的表现。然而，成功的法律案例匹配需要法律从业者的隐性知识，这些知识难以言传并编码至机器中。这突显了在高风险法律案例匹配中引入法律从业者的关键作用。为此，我们提出一种名为“Co-Matching”的协作匹配框架，鼓励机器与法律从业者共同参与匹配过程，以整合隐性知识。与仅依赖机器的现有方法不同，Co-Matching允许法律从业者和机器共同确定关键句子，并以概率方式组合这些句子。Co-Matching引入了一种名为ProtoEM的方法来估计人类决策的不确定性，从而促进概率组合。实验结果表明，Co-Matching持续优于现有法律案例匹配方法，相较于纯人工与纯机器匹配（分别平均提升+5.51%和+8.71%），带来了显著的性能改进。进一步分析显示，Co-Matching还确保了更优的人机协作效果。本研究在人机协作匹配任务中具有开创性意义，为未来协作匹配研究树立了里程碑。