Abstract
In previous research, knowledge selection tasks mostly rely on language model-based methods or knowledge ranking. However, approaches simply rely on the language model take all knowledge as sequential input that knowledge does not contain sequential information in most circumstances. On the other hand, the knowledge ranking method leverage dialog history and each given knowledge but not between pieces of knowledge. In the 10th Dialog System Technology Challenges (DSTC 10), we participated the second track of Knowledge-grounded Task-oriented Dialogue Modeling on Spoken Conversations. To deal with the problems mentioned above, we modified training methods based on SOTA models for the first and third sub-tasks and proposed Graph-Knowledge Selector (GKS), utilizing a graph-attention base model incorporated with language model for knowledge selection sub-task two. GKS makes knowledge selection decisions in the dialog by simultaneously considering each knowledge embedding generated from the language model, without sequential features. GKS also leverages considerable knowledge in the decision-making, takes relations across knowledge as a part of the selection process. GKS outperforms several SOTA models proposed in the data-set on knowledge selection from the 9th Dialog System Technology Challenges (DSTC9).
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URL
https://arxiv.org/abs/2112.03719
