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.

我们提出了一种多任务框架，作为会话中三个主要检索任务（个人选择、知识选择和回复选择）的通用检索器，通过设计一个双编码器体系结构来检索相关上下文，通过点积方法检索适合的候选项，并引入两个损失约束来捕捉对话上下文和不同候选项之间的微妙关系。实验证明我们的模型在训练领域内外都能取得最先进的检索质量，揭示了其成为同时用于不同候选项选择任务的通用检索器的潜在潜力和泛化能力。