  Textual response generation is an essential task for multimodal task-oriented
dialog systems.Although existing studies have achieved fruitful progress, they
still suffer from two critical limitations: 1) focusing on the attribute
knowledge but ignoring the relation knowledge that can reveal the correlations
between different entities and hence promote the response generation}, and 2)
only conducting the cross-entropy loss based output-level supervision but
lacking the representation-level regularization. To address these limitations,
we devise a novel multimodal task-oriented dialog system (named MDS-S2).
Specifically, MDS-S2 first simultaneously acquires the context related
attribute and relation knowledge from the knowledge base, whereby the
non-intuitive relation knowledge is extracted by the n-hop graph walk.
Thereafter, considering that the attribute knowledge and relation knowledge can
benefit the responding to different levels of questions, we design a
multi-level knowledge composition module in MDS-S2 to obtain the latent
composed response representation. Moreover, we devise a set of latent query
variables to distill the semantic information from the composed response
representation and the ground truth response representation, respectively, and
thus conduct the representation-level semantic regularization. Extensive
experiments on a public dataset have verified the superiority of our proposed
MDS-S2. We have released the codes and parameters to facilitate the research
community.
