In legal case retrieval, existing work has shown that human-mediated conversational search can improve users’ search experience. In practice, a suitable workflow can provide guidelines for constructing a machine-mediated agent replacing of human agents. Therefore, we conduct a comparison analysis and summarize two challenges when directly applying the conversational agent workflow in web search to legal case retrieval: (1) It is complex for agents to express their understanding of users’ information need. (2) Selecting a candidate case from the SERPs is more difficult for agents, especially at the early stage of the search process. To tackle these challenges, we propose a suitable conversational agent workflow in legal case retrieval, which contains two additional key modules compared with that in web search: Query Generation and Buffer Mechanism. A controlled user experiment with three control groups, using the whole workflow or removing one of these two modules, is conducted. The results demonstrate that the proposed workflow can actually support conversational agents working more efficiently, and help users save search effort, leading to higher search success and satisfaction for legal case retrieval. We further construct a large-scale dataset and provide guidance on the machine-mediated conversational search system for legal case retrieval.

在法律案件检索中，现有工作表明以人为中介的会话搜索可以改善用户的搜索体验。在实践中，一个合适的工作流程可以为构建一个机器介导的代理替代人工代理提供指导。因此，我们进行了比较分析并总结了将网络搜索中的会话代理工作流直接应用于法律案件检索时的两个挑战：（1）代理表达对用户信息需求的理解很复杂。(2) 从 SERP 中选择候选案例对于代理来说更加困难，尤其是在搜索过程的早期阶段。为了应对这些挑战，我们在法律案件检索中提出了一个合适的会话代理工作流程，与网络搜索相比，它包含两个额外的关键模块：查询生成和缓冲机制。使用整个工作流程或删除这两个模块中的一个，进行三个控制组的受控用户实验。结果表明，所提出的工作流实际上可以支持会话代理更有效地工作，并帮助用户节省搜索工作量，从而提高搜索成功率和对法律案件检索的满意度。我们进一步构建了一个大规模的数据集，并为用于法律案件检索的机器中介会话搜索系统提供指导。