School of Information Science and Electrical Engineering, Shandong Jiaotong University, Jinan 250357, China.
Sensors (Basel). 2023 Jul 30;23(15):6805. doi: 10.3390/s23156805.
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.
随着 ChatGPT 的流行，对话系统越来越受到关注。研究人员致力于设计一个能够像人类一样进行对话的知识型模型。传统的 seq2seq 对话模型通常存在性能有限和生成安全响应的问题。近年来，大规模预训练语言模型在各个领域展示了强大的能力。许多研究利用这些预训练模型来解决对话任务中的安全响应生成等问题。预训练模型可以通过在大规模数据上进行预训练来携带某些知识信息来增强响应。然而，当特定领域需要特定知识时，模型可能仍然会生成平淡或不适当的响应，并且此类模型的可解释性较差。因此，在本文中，我们提出了 KRP-DS 模型。我们设计了一个知识模块，将知识图谱作为对话系统中的外部知识纳入其中。该模块利用上下文信息进行路径推理，并指导知识预测。最后，预测的知识用于增强响应生成。实验结果表明，我们提出的模型可以有效地提高响应的质量和多样性，同时具有更好的可解释性，并且在自动评估和人工评估中均优于基线模型。
