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To improve the interactive capabilities of a dialogue system, e.g., to adaptto different customers, the Dialogue Robot Competition (DRC2022) was held. Asone of the teams, we built a dialogue system with a pipeline structurecontaining four modules. The natural language understanding (NLU) and naturallanguage generation (NLG) modules were GPT-2 based models, and the dialoguestate tracking (DST) and policy modules were designed on the basis ofhand-crafted rules. After the preliminary round of the competition, we foundthat the low variation in training examples for the NLU and failedrecommendation due to the policy used were probably the main reasons for thelimited performance of the system.

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