KRP-DS: A Knowledge Graph-Based Dialogue System with Inference-Aided Prediction

Autor: Qiang He, Shuobo Xu, Zhenfang Zhu, Peng Wang, Kefeng Li, Quanfeng Zheng, Yanshun Li
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Sensors, Vol 23, Iss 15, p 6805 (2023)
Druh dokumentu: article
ISSN: 23156805
1424-8220
74080059
DOI: 10.3390/s23156805
Popis: With the popularity of ChatGPT, there has been increasing attention towards dialogue systems. Researchers are dedicated to designing a knowledgeable model that can engage in conversations like humans. Traditional seq2seq dialogue models often suffer from limited performance and the issue of generating safe responses. In recent years, large-scale pretrained language models have demonstrated their powerful capabilities across various domains. Many studies have leveraged these pretrained models for dialogue tasks to address concerns such as safe response generation. Pretrained models can enhance responses by carrying certain knowledge information after being pre-trained on large-scale data. However, when specific knowledge is required in a particular domain, the model may still generate bland or inappropriate responses, and the interpretability of such models is poor. Therefore, in this paper, we propose the KRP-DS model. We design a knowledge module that incorporates a knowledge graph as external knowledge in the dialogue system. The module utilizes contextual information for path reasoning and guides knowledge prediction. Finally, the predicted knowledge is used to enhance response generation. Experimental results show that our proposed model can effectively improve the quality and diversity of responses while having better interpretability, and outperforms baseline models in both automatic and human evaluations.
Databáze: Directory of Open Access Journals
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje