面向任务型对话系统的双反馈知识检索
Dual-Feedback Knowledge Retrieval for Task-Oriented Dialogue Systems
AI总结：
针对任务型对话系统中知识检索与响应生成耦合导致的可扩展性挑战，提出检索器-生成器架构，并引入双反馈机制利用生成器输出的正负反馈作为伪标签训练检索器，在三个基准数据集上取得优越性能。
摘要：
高效的知识检索通过促进选择满足用户请求所需的相关信息，在确保端到端任务型对话系统的成功中发挥关键作用。然而，当前方法通常将知识检索与响应生成集成在一起，在处理大规模知识库时带来了可扩展性挑战。受开放域问答启发，我们提出了一种检索器-生成器架构，利用检索器检索相关知识，并利用生成器生成系统响应。由于缺乏检索器训练标签，我们提出依赖生成器的反馈作为伪标签来训练检索器。为此，我们引入了双反馈机制，基于生成器的输出生成正向与负向反馈。实验结果表明，我们的方法在三个基准数据集上的任务型对话任务中表现出优越性能。
英文摘要：
Efficient knowledge retrieval plays a pivotal role in ensuring the success of end-to-end task-oriented dialogue systems by facilitating the selection of relevant information necessary to fulfill user requests. However, current approaches generally integrate knowledge retrieval and response generation, which poses scalability challenges when dealing with extensive knowledge bases. Taking inspiration from open-domain question answering, we propose a retriever-generator architecture that harnesses a retriever to retrieve pertinent knowledge and a generator to generate system responses.~Due to the lack of retriever training labels, we propose relying on feedback from the generator as pseudo-labels to train the retriever. To achieve this, we introduce a dual-feedback mechanism that generates both positive and negative feedback based on the output of the generator. Our method demonstrates superior performance in task-oriented dialogue tasks, as evidenced by experimental results on three benchmark datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
