# Sarcasm Detection Base on Adaptive Incongruity Extraction Network and Incongruity Cross-Attention
## 4 Experiment
### Ablation Study  
In order to explore the impact of different components, ablation experiments are conducted to evaluate the performance of the adaptive incongruity semantic extraction module and the incongruity cross-attention module. Specifically, we consider three different scenarios, (1) remove the adaptive incongruity extraction module of text (denoted as w/o text_incon), (2) remove the adaptive incongruity extraction module of knowledge (denoted as w/o know_incon), and (3) remove the incongruity cross-attention that is the module of interaction between the text and knowledge (denoted as w/o cross_incon).  
The results are displayed in Table 5. Clearly, our model only performs best when all modules are included. From the results, we can see that each of our modules is indispensable and crucial for sarcasm detection. Discarding different modules, performance will deteriorate differently on different datasets. For example, Model (w/o text_incon) has 0.71% and 1.17% degradation on Twitter-G and Twitter-P, respectively, meanwhile, it has a 1.91% degradation on Reddit-Pol.  
In order to explore the effect of the gated unit, we maintain the normal operation of the other modules while examining the function of the gate module. We begin by removing the gate mechanism, leaving the remaining components intact. In this instance, Model (\(-\)gate) has 0.43%, 0.93%, and 1.96% degradation on Twitter-G, Twitter-P, and Reddit-Pol, respectively. To further investigate the performance of this module, we conduct comparison tests using the other two integration approaches. They are concatenation and element-wise addition. Model (+add) has 0.89%, 0.49%, and 1.35% degradation on Twitter-G, Twitter-P, and Reddit-Pol, respectively. Table 6 shows that merely extracting the features of text and knowledge does not achieve satisfactory results. How to effectively utilize the extracted feature information is also very critical. The absence of the gate mechanism leads to poorer performance, indicating the importance of the gate that can dynamically learn weights based on text and knowledge. It demonstrates that the optimum result cannot be achieved by using the integration method of concatenation and element-wise addition alone, which also demonstrates the effectiveness of the gated unit.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Twitter-G**} & \multicolumn{3}{c}{**Twitter-P**} & \multicolumn{3}{c}{**Reddit-Pol**} \\ \hline
**Method** & **P** & **R** & **F1** & **P** & **R** & **F1** & **P** & **R** & **F1** \\ \hline SDCK-maj & 0.8249 & 0.8376 & 0.8292 & 0.8516 & 0.8605 & 0.8546 & 0.7532 & 0.7519 & 0.7525 \\ Model-maj & 0.8689 & 0.8676 & 0.8682 & 0.8574 & 0.8615 & 0.8592 & 0.7640 & 0.7718 & 0.7656 \\ SDCK-min & 0.8552 & 0.8598 & 0.8573 & 0.8361 & 0.8426 & 0.8386 & 0.7555 & 0.7554 & 0.7555 \\ Model-min & 0.8683 & 0.8687 & 0.8685 & 0.8534 & 0.8587 & 0.8556 & 0.7656 & 0.7723 & 0.7675 \\ SDCK-con & 0.8349 & 0.8471 & 0.8392 & 0.8393 & 0.8413 & 0.8402 & 0.7610 & 0.7609 & 0.7610 \\ Model-con & 0.8746 & 0.8744 & 0.8746 & 0.8619 & 0.8632 & 0.8625 & 0.7647 & 0.7563 & 0.7595 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The experimental results of our incongruity extraction module are combined with the knowledge selection strategy.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Twitter-G** & **Twitter-P** & **Reddit-Pol** \\ \hline
**Method** & & **F1 Score** & \\ \hline Method (w/o text_incon) & 0.8680 & 0.8495 & 0.7467 \\ Method (w/o know_incon) & 0.8730 & 0.8560 & 0.7406 \\ Method (w/o cross_incon) & 0.8685 & 0.8456 & 0.7506 \\
**Method (ours)** & **0.8751** & **0.8612** & **0.7658** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Experimental results of the ablation study. The best result is in boldface.