This paper focuses on sarcasm detection, which aims to identify whether given statements convey criticism, mockery, or other negative sentiment opposite to the literal meaning. To detect sarcasm, humans often require a comprehensive understanding of the semantics in the statement and even resort to external commonsense to infer the fine-grained incongruity. However, existing methods lack commonsense inferential ability when they face complex real-world scenarios, leading to unsatisfactory performance. To address this problem, we propose a novel framework for sarcasm detection, which conducts incongruity reasoning based on commonsense augmentation, called EICR. Concretely, we first employ retrieval-augmented large language models to supplement the missing but indispensable commonsense background knowledge. To capture complex contextual associations, we construct a dependency graph and obtain the optimized topology via graph refinement. We further introduce an adaptive reasoning skeleton that integrates prior rules to extract sentiment-inconsistent subgraphs explicitly. To eliminate the possible spurious relations between words and labels, we employ adversarial contrastive learning to enhance the robustness of the detector. Experiments conducted on five datasets demonstrate the effectiveness of EICR.

翻译：本文聚焦于讽刺检测，其旨在识别给定陈述是否传达与字面意义相反的批评、嘲讽或其他负面情感。为检测讽刺，人类通常需要全面理解陈述中的语义，甚至借助外部常识来推断细粒度的不一致性。然而，现有方法在面对复杂现实场景时缺乏常识推理能力，导致性能不尽如人意。为解决此问题，我们提出一种新颖的讽刺检测框架EICR，该框架基于常识增强进行不一致性推理。具体而言，我们首先采用检索增强的大型语言模型来补充缺失但不可或缺的常识背景知识。为捕捉复杂的上下文关联，我们构建依存图并通过图优化获得最优拓扑结构。我们进一步引入集成先验规则的自适应推理骨架，以显式提取情感不一致子图。为消除词语与标签间可能存在的伪关联，我们采用对抗对比学习来增强检测器的鲁棒性。在五个数据集上进行的实验证明了EICR的有效性。