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.

提出了一种名为 MDS-S2 的新型多模式任务导向对话系统，通过从知识库中获取相关属性和关系知识，设计了多级知识组成模块以及一组潜在查询变量以实现表征级别的语义规范化来解决文本响应生成过程中的关键问题。