Improving the Robustness of Knowledge-Grounded Dialogue via Contrastive
  Learning
Jiaan Wang Jianfeng Qu Kexin Wang Zhixu Li Wen Hua Ximing Li An Liu
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might contain erroneous and outdated facts.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.