TFCD: Towards Multi-modal Sarcasm Detection via Training-Free Counterfactual Debiasing

Multi-modal sarcasm detection (MSD), which aims to identify whether a given sample with multi-modal information (i.e., text and image) is sarcastic, has garnered widespread attention. Recent approaches focus on designing sophisticated architectures or mechanisms to extract sarcastic cues from entire or local image and text features. Nevertheless, a long-overlooked issue is that current MSD task invariably suffers from unintended dataset biases, especially the statistical label bias and sarcasmless word bias. Concretely, such harmful biases are confounders that may mislead existing models to learn spurious correlations, significantly limiting models' performance. To tackle this issue, this paper proposes a Training-Free Counterfactual Debiasing framework TFCD, which first formulates the causalities among variables in MSD via a tailored causal graph. Then, TFCD extracts biases from the conventionally-trained model by generating counterfactual utterances and contexts and mitigates them using element-wise subtraction. Extensive experiments on two benchmarks demonstrate the effectiveness of the proposed TFCD. Remarkably, TFCD requires neither data balancing nor model modifications, and thus can be seamlessly integrated into diverse state-of-the-art approaches and achieve considerable improvement margins.