Paper ID: 2210.09518

Team Flow at DRC2022: Pipeline System for Travel Destination Recommendation Task in Spoken Dialogue

Ryu Hirai, Atsumoto Ohashi, Ao Guo, Hideki Shiroma, Xulin Zhou, Yukihiko Tone, Shinya Iizuka, Ryuichiro Higashinaka

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.

Submitted: Oct 18, 2022