  To improve the interactive capabilities of a dialogue system, e.g., to adapt
to different customers, the Dialogue Robot Competition (DRC2022) was held. As
one of the teams, we built a dialogue system with a pipeline structure
containing four modules. The natural language understanding (NLU) and natural
language generation (NLG) modules were GPT-2 based models, and the dialogue
state tracking (DST) and policy modules were designed on the basis of
hand-crafted rules. After the preliminary round of the competition, we found
that the low variation in training examples for the NLU and failed
recommendation due to the policy used were probably the main reasons for the
limited performance of the system.
