In previous research, knowledge-selection tasks mostly rely on language
model-based methods or knowledge ranking. However, while approaches that rely
on the language models take all knowledge as sequential input, knowledge does
not contain sequential information in most circumstances. On the other hand,
the knowledge-ranking methods leverage dialog history and each given knowledge
snippet separately, but they do not consider information between knowledge
snippets. In the Tenth Dialog System Technology Challenges (DSTC10), we
participated in the second Knowledge-grounded Task-oriented Dialogue Modeling
on Spoken Conversations. To deal with the problems mentioned above, we modified
training methods based on state-of-the-art (SOTA) models for the first and
third sub-tasks. As for the second sub-task of knowledge selection, we proposed
Graph-Knowledge Selector (GKS), utilizing a graph-attention base model
incorporated with the language model. GKS makes knowledge-selection decisions
in the dialog by simultaneously considering each knowledge embedding generated
from the language model without sequential features. Moreover, GKS leverages
considerable knowledge in decision-making and takes relations across knowledge
as part of the selection process. As a result, GKS outperforms several SOTA
models proposed in the data-set on knowledge selection from the Ninth Dialog
System Technology Challenges (DSTC9).

本论文基于当代最先进的语言模型构建了 Graph-Knowledge Selector（GKS）来解决对话系统中知识选择的问题，并考虑了知识之间的联系，明显优于 Ninth Dialog System Technology Challenges（DSTC9）中提出的几种最新技术模型。