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 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.
EMNLP