  Existing methods for Dialogue Response Generation (DRG) in Task-oriented
Dialogue Systems (TDSs) can be grouped into two categories: template-based and
corpus-based. The former prepare a collection of response templates in advance
and fill the slots with system actions to produce system responses at runtime.
The latter generate system responses token by token by taking system actions
into account. While template-based DRG provides high precision and highly
predictable responses, they usually lack in terms of generating diverse and
natural responses when compared to (neural) corpus-based approaches.
Conversely, while corpus-based DRG methods are able to generate natural
responses, we cannot guarantee their precision or predictability. Moreover, the
diversity of responses produced by today's corpus-based DRG methods is still
limited. We propose to combine the merits of template-based and corpus-based
DRGs by introducing a prototype-based, paraphrasing neural network, called
P2-Net, which aims to enhance quality of the responses in terms of both
precision and diversity. Instead of generating a response from scratch, P2-Net
generates system responses by paraphrasing template-based responses. To
guarantee the precision of responses, P2-Net learns to separate a response into
its semantics, context influence, and paraphrasing noise, and to keep the
semantics unchanged during paraphrasing. To introduce diversity, P2-Net
randomly samples previous conversational utterances as prototypes, from which
the model can then extract speaking style information. We conduct extensive
experiments on the MultiWOZ dataset with both automatic and human evaluations.
The results show that P2-Net achieves a significant improvement in diversity
while preserving the semantics of responses.
