Pretrained language models (PLMs) based knowledge-grounded dialogue systems are prone to generate responses that are factually inconsistent with the provided knowledge source. In such inconsistent responses, the dialogue models fail to accurately express the external knowledge they rely upon. Inspired by previous work which identified that feed-forward networks (FFNs) within Transformers are responsible for factual knowledge expressions, we investigate two methods to efficiently improve the factual expression capability {of FFNs} by knowledge enhancement and alignment respectively. We first propose \textsc{K-Dial}, which {explicitly} introduces {extended FFNs in Transformers to enhance factual knowledge expressions} given the specific patterns of knowledge-grounded dialogue inputs. Additionally, we apply the reinforcement learning for factual consistency (RLFC) method to implicitly adjust FFNs' expressions in responses by aligning with gold knowledge for the factual consistency preference. To comprehensively assess the factual consistency and dialogue quality of responses, we employ extensive automatic measures and human evaluations including sophisticated fine-grained NLI-based metrics. Experimental results on WoW and CMU\_DoG datasets demonstrate that our methods efficiently enhance the ability of the FFN module to convey factual knowledge, validating the efficacy of improving factual consistency for knowledge-grounded dialogue systems.

翻译：基于预训练语言模型的知识驱动对话系统容易生成与给定知识源事实不一致的回复。在这种不一致的回复中，对话模型无法准确表达其依赖的外部知识。受先前发现Transformer中前馈网络负责事实知识表达的研究启发，我们研究了两种方法，分别通过知识增强和对齐高效提升前馈网络的事实表达能力。我们首先提出K-Dial方法，针对知识驱动对话输入的特殊模式，显式地在Transformer中引入扩展前馈网络以增强事实知识表达。此外，我们应用基于强化学习的事实一致性方法，通过对齐黄金知识隐式调整前馈网络在回复中的表达，以符合事实一致性偏好。为了全面评估回复的事实一致性和对话质量，我们采用了广泛的自动评测和人工评估，包括基于细粒度自然语言推理的先进指标。在WoW和CMU_DoG数据集上的实验结果表明，我们的方法能高效提升前馈网络模块传递事实知识的能力，验证了改进知识驱动对话系统事实一致性的有效性。