论文标题
DRC2022的团队流程：旅行目的地推荐任务的管道系统在口语对话中
Team Flow at DRC2022: Pipeline System for Travel Destination Recommendation Task in Spoken Dialogue
论文作者
论文摘要
为了提高对话系统的互动功能，例如，为了适应不同的客户，对话机器人竞争（DRC2022）。作为一个团队，我们建立了一个对话系统，其中包含四个模块的管道结构。自然语言理解（NLU）和自然语言生成（NLG）模块是基于GPT-2的模型，对话状态跟踪（DST）和策略模块的设计是根据手工制作的规则设计的。在比赛的初步回合之后，我们发现，由于所使用的政策而导致的NLU训练示例的差异很小，可能是该系统性能有限的主要原因。
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.
