Knowledge-grounded dialogue (KGD) learns to generate an informative response
based on a given dialogue context and external knowledge (\emph{e.g.},
knowledge graphs; KGs). Recently, the emergence of large language models (LLMs)
and pre-training techniques has brought great success to knowledge-grounded
dialogue. However, when building KGD systems in real applications, there are
various real-world noises that are inevitable to face. For example, the
dialogue context might involve perturbations such as misspellings and
abbreviations. In addition, KGs typically suffer from incompletion and also
might contain erroneous and outdated facts. Such real-world noises pose a
challenge to the robustness of KGD systems and hinder their applications in the
real world. In this paper, we propose an entity-based contrastive learning
framework for improving the robustness of KGD. Specifically, we make use of the
entity information in a KGD sample to create both its positive and negative
samples which involve semantic-irrelevant and semantic-relevant perturbations,
respectively. The contrastive learning framework ensures the KGD model is aware
of these two types of perturbations, thus generating informative responses with
the potentially noisy inputs in real applications. Experimental results on
three benchmark datasets show that our method achieves new state-of-the-art
performance in terms of automatic evaluation scores, verifying its
effectiveness and potentiality. Furthermore, we show that our method can
generate better responses than comparison models in both the noisy and the
few-shot settings.

基于对话文本和外部知识的知识驱动对话系统面临实际应用中各种噪声干扰，为提高系统鲁棒性，我们提出了一种基于实体对比学习的框架，通过引入两种类型的干扰样本，使得模型在真实应用中能够生成具有信息性的回应，实验证明该方法在三个基准数据集上实现了最新的性能提升，并在噪声和少样本场景下胜过其他模型。