  Despite commendable achievements made by existing work, prevailing multimodal
sarcasm detection studies rely more on textual content over visual information.
It unavoidably induces spurious correlations between textual words and labels,
thereby significantly hindering the models' generalization capability. To
address this problem, we define the task of out-of-distribution (OOD)
multimodal sarcasm detection, which aims to evaluate models' generalizability
when the word distribution is different in training and testing settings.
Moreover, we propose a novel debiasing multimodal sarcasm detection framework
with contrastive learning, which aims to mitigate the harmful effect of biased
textual factors for robust OOD generalization. In particular, we first design
counterfactual data augmentation to construct the positive samples with
dissimilar word biases and negative samples with similar word biases.
Subsequently, we devise an adapted debiasing contrastive learning mechanism to
empower the model to learn robust task-relevant features and alleviate the
adverse effect of biased words. Extensive experiments show the superiority of
the proposed framework.
