  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.
