Context-Aware Emotion Recognition (CAER) is a crucial and challenging task that aims to perceive the emotional states of the target person with contextual information. Recent approaches invariably focus on designing sophisticated architectures or mechanisms to extract seemingly meaningful representations from subjects and contexts. However, a long-overlooked issue is that a context bias in existing datasets leads to a significantly unbalanced distribution of emotional states among different context scenarios. Concretely, the harmful bias is a confounder that misleads existing models to learn spurious correlations based on conventional likelihood estimation, significantly limiting the models' performance. To tackle the issue, this paper provides a causality-based perspective to disentangle the models from the impact of such bias, and formulate the causalities among variables in the CAER task via a tailored causal graph. Then, we propose a Contextual Causal Intervention Module (CCIM) based on the backdoor adjustment to de-confound the confounder and exploit the true causal effect for model training. CCIM is plug-in and model-agnostic, which improves diverse state-of-the-art approaches by considerable margins. Extensive experiments on three benchmark datasets demonstrate the effectiveness of our CCIM and the significance of causal insight.

翻译：上下文感知情绪识别（CAER）是一项关键且富有挑战性的任务，旨在通过上下文信息感知目标人物的情绪状态。现有方法通常专注于设计复杂的架构或机制，从主体和上下文中提取看似有意义的表示。然而，一个长期被忽视的问题是，现有数据集中的上下文偏差导致不同情境场景下情绪状态分布显著不平衡。具体而言，这种有害偏差是一种混杂因子，会误导现有模型基于常规似然估计学习虚假相关性，从而严重限制模型性能。为解决这一问题，本文提出了一种基于因果关系的视角，将模型从此类偏差的影响中解耦，并通过定制的因果图形式化CAER任务中变量之间的因果关系。随后，我们基于后门调整提出了一种上下文因果干预模块（CCIM），以消除混杂因子并利用真实因果效应进行模型训练。CCIM即插即用且与模型无关，能显著提升多种最先进方法的性能。在三个基准数据集上的大量实验证明了CCIM的有效性及因果洞见的重要性。