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 In addition, KGs typically suffer from incompletion and also might contain erroneous and outdated facts.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.

翻译：知识驱动对话（KGD）旨在基于给定的对话上下文和外部知识（如知识图谱）生成信息丰富的回复。近年来，大语言模型和预训练技术的出现为知识驱动对话带来了巨大成功。然而，在实际应用中构建KGD系统时，不可避免地面临各种现实噪声。例如，对话上下文可能包含拼写错误、缩写等扰动。此外，知识图谱通常存在不完备性，且可能包含错误或过时的事实。这些现实噪声对KGD系统的鲁棒性构成挑战，阻碍了其实际应用。本文提出一种基于实体的对比学习框架，用于提升KGD的鲁棒性。具体而言，我们利用KGD样本中的实体信息创建其正样本和负样本，分别涉及语义无关扰动和语义相关扰动。该对比学习框架确保KGD模型能够感知这两类扰动，从而在实际应用中利用可能含噪声的输入生成信息丰富的回复。在三个基准数据集上的实验结果表明，我们的方法在自动评估分数上取得了新的最优性能，验证了其有效性和潜力。此外，我们证明该方法在含噪声和少样本场景下均能生成优于对比模型的回复。