Jinyoung Kim  Hyunmook Cha  Youngjoong Ko

10.5626/JOK.2024.51.10.882

Task-Oriented Dialogue response generation dialogue state tracking graph Attention Mechanism

The field of Task-Oriented Dialogue Systems focuses on using natural language processing to assist users in achieving specific tasks through conversation. Recently, transformer-based pre-trained language models have been employed to enhance performances of task-oriented dialogue systems. This paper proposes a response generation model based on Graph Attention Networks (GAT) to integrate external knowledge data into transformer-based language models for more specialized responses in dialogue systems. Additionally, we extend this research to incorporate information from multiple graphs, leveraging information from more than two graphs. We also collected and refined dialogue data based on music domain knowledge base to evaluate the proposed model. The collected dialogue dataset consisted of 2,076 dialogues and 226,823 triples. In experiments, the proposed model showed a performance improvement of 13.83%p in ROUGE-1, 8.26%p in ROUGE-2, and 13.5%p in ROUGE-L compared to the baseline KoBART model on the proposed dialogue dataset.