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.

PLMs 基于知识驱动的对话系统存在生成与提供的知识源不一致的回应问题，本文通过知识增强和对齐两种方法有效提升了前馈网络（FFNs）在回应中表达事实知识的能力，验证了提升知识驱动对话系统的事实一致性的有效性。