Retrieval-guided Dialogue Response Generation via a Matching-to-Generation Framework
End-to-end sequence generation is a popular technique for developing open domain dialogue systems, though they suffer from the \textit{safe response problem}. Researchers have attempted to tackle this problem by incorporating generative models with the returns of retrieval systems. Recently, a skeleton-then-response framework has been shown promising results for this task. Nevertheless, how to precisely extract a skeleton and how to effectively train a retrieval-guided response generator are still challenging. This paper presents a novel framework in which the skeleton extraction is made by an interpretable matching model and the following skeleton-guided response generation is accomplished by a separately trained generator. Extensive experiments demonstrate the effectiveness of our model designs.
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