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所提供知识源事实不一致的回复。在这种不一致的回复中，对话模型未能准确表述其依赖的外部知识。受先前研究表明Transformer中的前馈网络负责事实知识表达这一发现的启发，我们分别通过知识增强和对齐两种方法，研究如何有效改进前馈网络的事实表达能力。我们首先提出K-Dial方法，该方法显式地在Transformer中引入扩展的前馈网络，以增强针对知识驱动对话输入特定模式的事实知识表达。此外，我们应用面向事实一致性的强化学习方法，通过对齐黄金知识来隐式调整前馈网络在回复中的表达，以满足事实一致性偏好。为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复杂指标。在WoW和CMU_DoG数据集上的实验结果表明，我们的方法有效增强了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。