## 6 Conclusions  
We studied the redundant information problem of video modalities in multimodal sarcasm detection. We constructed an EFAFN multimodal sarcasm detection model that used the open-source sarcasm detection dataset MUStARD for experiments and proposed a data augmentation method to solve the overfitting problem during model training. In addition, a ViViT-VAT model was also constructed for comparative experiments. The final experimental results show that the EFAFN multimodal sarcasm detection model outperforms the ViViT-VAT model, proving the effectiveness of our model. The model based on facial features outperforms the model based on whole image features, demonstrating the importance of facial information in sarcasm detection. Ablation experiment results show that not using data augmentation methods and adaptive fusion strategies will cause model performance degradation, illustrating the necessity of our proposed two components. The use of global speech features and unaveraged local speech features also degrades the model performance, indicating that the operation of averaging features will highlight the sarcasm features of speech modalities.  
Although the model's effectiveness in this paper, the usefulness of the three modalities, and the necessity of the adaptive fusion strategy are proved by experiments, there is still room for further optimization. First, this paper only removes information irrelevant to sarcasm detection for video modalities, and then we can discuss redundant information removal algorithms for speech and text modalities. Second, some multimodal information may not be helpful for sarcasm detection. We will consider designing better processing methods for text and speech modalities in the future. For example, the speech data in the dataset are augmented with additional data, such as emojis [11], to add valuable information for sarcasm detection. Third, the dataset used in this paper only contains three modalities, and the influence of other modalities' data, such as text in images, on sarcasm detection is not considered. Other existing public datasets can be used in the future.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F1-Score** & **Standard Deviation (F1-Score)** \\ \hline SCE [30] & 72.3 & 72.3 & 72.3 & - \\ Model(\(V^{*}\)) & 77.5 & 77.4 & 77.3 & 1.10 \\ Model(A\_N) & 78.5 & 78.0 & 77.9 & 1.03 \\ Model(A\_O) & 73.9 & 73.8 & 73.7 & 0.72 \\ Ours & **79.0** & **79.0** & **79.0** & **0.54** \\ \hline \hline \end{tabular}
\end{table}
Table 8: Ablation experiment results. \(V^{*}\) represents the contextual video features collected by [PERSON] et al. [9].  
**Author Contributions:** Data curation, H.Z.; Formal analysis, S.Y.; Funding acquisition, Y.S. and J.W.; Methodology, Y.S. and H.Z.; Software, Y.S.; Supervision, Y.S. and J.W.; Writing--original draft, H.Z. and S.Y.; writing--review and editing, Y.S. and J.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by The National Natural Science Foundation of China (62072335) and The Tianjin Science and Technology Program (19 PTZWHZ00020).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The dataset can be obtained from [[https://github.com/soujanyaporia/MUStARD](https://github.com/soujanyaporia/MUStARD)]([https://github.com/soujanyaporia/MUStARD](https://github.com/soujanyaporia/MUStARD)), accessed on 26 July 2021.  
**Acknowledgments:** We really appreciate the valuable comments from the anonymous reviewers, which have made the paper more comprehensive.  
**Conflicts of Interest:** The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.