  Pre-trained language models (PLM) have advanced the state-of-the-art across
NLP applications, but lack domain-specific knowledge that does not naturally
occur in pre-training data. Previous studies augmented PLMs with symbolic
knowledge for different downstream NLP tasks. However, knowledge bases (KBs)
utilized in these studies are usually large-scale and static, in contrast to
small, domain-specific, and modifiable knowledge bases that are prominent in
real-world task-oriented dialogue (TOD) systems. In this paper, we showcase the
advantages of injecting domain-specific knowledge prior to fine-tuning on TOD
tasks. To this end, we utilize light-weight adapters that can be easily
integrated with PLMs and serve as a repository for facts learned from different
KBs. To measure the efficacy of proposed knowledge injection methods, we
introduce Knowledge Probing using Response Selection (KPRS) -- a probe designed
specifically for TOD models. Experiments on KPRS and the response generation
task show improvements of knowledge injection with adapters over strong
baselines.
