通过常识推理检测反讽的情感不一致性
Detecting Emotional Incongruity of Sarcasm by Commonsense Reasoning
AI总结：
本文提出反讽检测框架EICR，通过检索增强大模型补充常识、构建依赖图、引入自适应推理骨架提取情感不一致子图，并结合对抗对比学习增强鲁棒性，在五个数据集上验证了有效性。
摘要：
本文聚焦于反讽检测，旨在识别给定陈述是否传达了与字面意思相反的批评、嘲讽或其他负面情感。为了检测反讽，人类通常需要对陈述的语义有全面的理解，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的现实场景时缺乏常识推理能力，导致性能不佳。为了解决这个问题，我们提出了一种用于反讽检测的新框架，该框架基于常识增强进行不一致性推理，称为EICR。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。为了捕捉复杂的上下文关联，我们构建了一个依赖图，并通过图细化获得优化的拓扑结构。我们进一步引入了一个自适应推理骨架，该骨架集成了先验规则以显式地提取情感不一致的子图。为了消除词与标签之间可能的虚假关系，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。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
