Conversational retrieval refers to an information retrieval system that operates in an iterative and interactive manner, requiring the retrieval of various external resources, such as persona, knowledge, and even response, to effectively engage with the user and successfully complete the dialogue. However, most previous work trained independent retrievers for each specific resource, resulting in sub-optimal performance and low efficiency. Thus, we propose a multi-task framework function as a universal retriever for three dominant retrieval tasks during the conversation: persona selection, knowledge selection, and response selection. To this end, we design a dual-encoder architecture consisting of a context-adaptive dialogue encoder and a candidate encoder, aiming to attention to the relevant context from the long dialogue and retrieve suitable candidates by simply a dot product. Furthermore, we introduce two loss constraints to capture the subtle relationship between dialogue context and different candidates by regarding historically selected candidates as hard negatives. Extensive experiments and analysis establish state-of-the-art retrieval quality both within and outside its training domain, revealing the promising potential and generalization capability of our model to serve as a universal retriever for different candidate selection tasks simultaneously.

翻译：对话检索是指一种以迭代和交互方式运行的信息检索系统，需要检索各种外部资源（如角色、知识甚至回复），以有效与用户互动并成功完成对话。然而，以往大多数研究为每种特定资源训练独立的检索器，导致性能次优且效率低下。为此，我们提出一个多任务框架，作为对话中三种主要检索任务的通用检索器：角色选择、知识选择和回复选择。为实现这一目标，我们设计了一种双编码器架构，包含上下文自适应对话编码器和候选编码器，旨在通过简单的点积计算关注长对话中的相关上下文并检索合适的候选。此外，我们引入两种损失约束，通过将历史上选中的候选视为硬负例，来捕捉对话上下文与不同候选之间的微妙关系。大量实验和分析表明，我们的模型在训练领域内外均达到了最先进的检索质量，揭示了其同时作为多种候选选择任务的通用检索器的潜力与泛化能力。