# Sarcasm Detection Base on Adaptive Incongruity Extraction Network and Incongruity Cross-Attention
## 4 Experiment
### Experimental Results  
In this part, our model is compared with baselines based on normal evaluation metrics, such as precision, recall, and F1 score. Then, we analyze the experimental results of our model with the adaptive incongruity extraction module, the incongruity cross-attention module, and the gate module. The results on the three datasets demonstrate that our model improves the state-of-the-art performance substantially. Our model outperforms earlier benchmarks in precision, recall, and F1 score, and the results perform differently on different datasets. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Learning-Rate** & **Train-Batch** & **MaxSL** & **MaxKL** & **Train-Epochs** \\ \hline Twitter-G & \(5\times 10^{-5}\) & 30 & 40 & 20 & 4 \\ Twitter-P & \(2\times 10^{-5}\) & 30 & 40 & 27 & 6 \\ Reddit-Pol & \(5\times 10^{-5}\) & 30 & 40 & 20 & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Different hyperparameters for different datasets in the experiment. The performance of our method and the other benchmark models on the three datasets is displayed in Table 3. The experimental results of baseline models are collected from [PERSON] et al. [13]. Obviously, our method obtains the best results across all datasets. To be specific, our model achieves 5.34%, 1.88%, and 3.84% improvement on the datasets of Twitter-G, Twitter-P, and Reddte-pol, respectively, compared to the fine-tuned BERT base model on the F1 metric. SDCK-maj, SDCK-min, and SDCK-con represent the model of SDCK [13] under majority, minority, and contrast knowledge selection methods of sentiment-based, respectively. SDCK-att represents the model of SDCK [13] under the attention-based knowledge selection method. [PERSON] et al. [13] also introduces commonsense knowledge, but they need several knowledge selection strategies. Moreover, in their work, one method can only work well on a certain dataset [13]. We can achieve good results on all three datasets simultaneously and far surpass their best by just using our model. For example, on the datasets of Twitter-G, Twitter-P, and Reddte-pol, our model achieves 4.59%, 0.66%, and 1.33% improvement, respectively, compared to the SDCK-maj model on the F1 metric. Similarly, our model achieves 3.58%, 2.1%, and 0.48% improvement compared to the SDCK-con model on the F1 metric. The three methods, SDCK-maj, SDCK-min, and SDCK-con correspond to three selection strategies [13]. Each method can only achieve good results on a certain dataset, and we far exceed their best results. Their knowledge selection strategy has inferior generalization performance [13], and it must use the corresponding knowledge selection method to select the required knowledge in advance and store it in the document, demonstrating the complexity of their method [13]. In contrast, our method is more straightforward and user-friendly, and it yields superior experimental results. Compared with their implicit policy model SDCK-att, on the datasets of Twitter-G, Twitter-P, and Reddit-Pol, our model achieves 3.66%, 2.02%, and 0.87% improvement, respectively. It shows that our model functions much better than the traditional self-attention mechanism method in incongruriity extraction. To further demonstrate the validity of our model, we undertake experiments on SDCK-maj, SDCK-min, and SDCK-con models [13], respectively. We add our two key modules (adaptive incongruriity extraction module and incongruriity cross-attention module) to the methods [13]. Table 4 provides the experimental comparison before and after the addition of our two key modules. Model-maj, Model-min, and Model-con denote the models that SDCK-maj, SDCK-min, and SDCK-con are combined with in our two key modules, respectively. Our two key modules significantly improved the performance of their models [13] on almost all datasets. For example, on Twitter-G, Twitter-P, and Reddit-Pol datasets, Model-maj achieves 3.9%, 0.46%, and 1.31% improvement compared to the