Multimodal sarcasm detection has received considerable attention due to its unique role in social networks. Existing methods often rely on feature concatenation to fuse different modalities or model the inconsistencies among modalities. However, sarcasm is often embodied in local and momentary nuances in a subtle way, which causes difficulty for sarcasm detection. To effectively incorporate these nuances, this paper presents Context-Aware Self-Attention Fusion (CAAF) to integrate local and momentary multimodal information into specific words. Furthermore, due to the instantaneous nature of sarcasm, the connotative meanings of words post-multimodal integration generally deviate from their denotative meanings. Therefore, Word Weight Calculation (WWC) is presented to compute the weight of specific words based on CAAF’s fusion nuances, illustrating the inconsistency between connotation and denotation. We evaluate our method on the MUStARD dataset, achieving an accuracy of 76.9 and an F1 score of 76.1, which surpasses the current state-of-the-art IWAN model by 1.7 and 1.6 respectively.

Recently, fine-tuning the pre-trained language model (PrLM) on labeled sentiment datasets demonstrates impressive performance. However, collecting labeled sentiment dataset is time-consuming, and fine-tuning the whole PrLM brings about much computation cost. To this end, we focus on multi-source unsupervised sentiment adaptation problem with the pre-trained features, which is more practical and challenging. We first design a dynamic feature network to fully exploit the extracted pre-trained features for efficient domain adaptation. Meanwhile, with the difference of the traditional source-target domain alignment methods, we propose a novel asymmetric mutual learning strategy, which can robustly estimate the pseudo-labels of the target domain with the knowledge from all the other source models. Experiments on multiple sentiment benchmarks show that our method outperforms the recent state-of-the-art approaches, and we also conduct extensive ablation studies to verify the effectiveness of each the proposed module.