Dialogue systems can leverage large pre-trained language models and knowledge to generate fluent and informative responses. However, these models are still prone to produce hallucinated responses not supported by the input source, which greatly hinders their application. The heterogeneity between external knowledge and dialogue context challenges representation learning and source integration, and further contributes to unfaithfulness. To handle this challenge and generate more faithful responses, this paper presents RHO ($\rho$) utilizing the representations of linked entities and relation predicates from a knowledge graph (KG). We propose (1) local knowledge grounding to combine textual embeddings with the corresponding KG embeddings; and (2) global knowledge grounding to equip RHO with multi-hop reasoning abilities via the attention mechanism. In addition, we devise a response re-ranking technique based on walks over KG sub-graphs for better conversational reasoning. Experimental results on OpenDialKG show that our approach significantly outperforms state-of-the-art methods on both automatic and human evaluation by a large margin, especially in hallucination reduction (17.54% in FeQA).

翻译：对话系统可利用大规模预训练语言模型与知识生成流畅且信息丰富的回复。然而，此类模型仍容易产生不受输入源支持的幻觉性回复，严重制约了其实际应用。外部知识与对话语境之间的异质性对表征学习及源信息融合构成挑战，进而导致生成内容偏离事实。为应对这一挑战并生成更可靠的回复，本文提出RHO ($\rho$) 方法，利用知识图谱中链接实体与关系谓词的表征。我们提出：(1) 局部知识归约，将文本嵌入与对应知识图谱嵌入相结合；(2) 全局知识归约，通过注意力机制使RHO具备多跳推理能力。此外，我们设计了一种基于知识图谱子图游走的回复重排序技术，以增强对话推理能力。在OpenDialKG数据集上的实验结果表明，本方法在自动评估与人工评估中均显著优于现有最优方法，尤其在幻觉抑制方面（FeQA指标提升17.54%）。