# Hate Speech Detection by Using Rationales for Judging Sarcasm
## 6 Discussion  
The main results are reported in Table 2.  
**Performance:** The results indicate a gradual enhancement in the performance metrics, with the macro F1 score exhibiting a marginal increment of 0.01 alongside improvements in other metrics when utilizing the proposed method in combination with sarcasm rationale. Figure 7 provides a detailed breakdown of the performance results via a confusion matrix. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Performance**} & \multicolumn{3}{c}{**Explanability**} \\ \cline{2-9}  & & & & **Plausibility** & & **Faithfulness** \\ \cline{2-9}  & **Acc. \(\uparrow\)** & **Macro F1 \(\uparrow\)** & **AUROC \(\uparrow\)** & **IOU F1 \(\uparrow\)** & **Token F1 \(\uparrow\)** & **AUPRC \(\uparrow\)** & **Comp. \(\uparrow\)** & **Suff. \(\downarrow\)** \\ \hline Baseline & 0.7425 & 0.6883 & 0.8264 & 0.0577 & 0.2799 & 0.3884 & 0.1789 & 0.1363 \\ \hline Proposed Method & **0.7492** & **0.6983** & **0.8450** & 0.0429 & **0.3232** & **0.5667** & \(-\)0.0655 & **0.0546** \\ \hline \hline \end{tabular}  
* The symbol \(\uparrow\) denotes improvement in metric values when there is an increase, whereas the symbol \(\downarrow\) signifies enhancement when there is a decrease in metric values.  
\end{table} 
Table 2: Model’s performance and explanability result. Figure 7: (**a**) Confusion matrix for baseline method’s result. (**b**) Confusion matrix for proposed method’s result. Performance metrics from the confusion matrix are illustrated in Figure 7a,b for both the baseline and the proposed method using True Positives (TP), False Positives (FP), False Negatives (FN), and True Negatives (TN) for each class. _Baseline Performance Metrics Calculation_  
_Precision:_  
For hatespeech: \(P=0.796\)  
For offensive: \(P=0.533\)  
For normal: \(P=0.811\)  
_Recall:_  
For hatespeech: \(R=0.796\)  
For offensive: \(R=0.480\)  
For normal: \(R=0.795\)  
_F1-Score:_  
For hatespeech: \(F1=0.796\)  
For offensive: \(F1=0.506\)  
For normal \(F1=0.803\)  
_Proposed Method Performance Metrics Calculation_  
_Precision:_  
For hatespeech: \(P=0.815\)  
For offensive: \(P=0.520\)  
For normal: \(P=0.814\)  
_Recall:_  
For hatespeech: \(R=0.778\)  
For offensive: \(R=0.500\)  
For normal: \(R=0.781\)  
_F1-Score:_  
For hatespeech: \(F1=0.796\)  
For offensive: \(F1=0.510\)  
For normal: \(F1=0.797\)  
The performance metrics from both the baseline and proposed method revealed a comparison in their effectiveness. The proposed method demonstrated improvements in precision for the hate speech and normal speech categories compared to the baseline. However, there was a slight decrease in precision for offensive language classification. The recall for all categories remained relatively similar between the baseline and proposed methods. Overall, the F1-scores for the hate speech and normal speech categories remained consistent, while there was a slight increase in the F1-score for offensive language classification with the proposed method. **Explainability:** A significant improvement was evident in three out of five explainability metrics, encompassing both plausibility and faithfulness. Within the realm of explainability metrics, notable advancements were observed in two metrics (token F1 and AUPRC) under plausibility and sufficiency under faithfulness. The HateXplain [13] paper demonstrates that using rationale compared to not using it, alone, resulted in an increase in only one out of five explainability metrics for the BERT model, specifically in comprehensiveness within faithfulness. In contrast, our dataset and proposed method enhanced three metrics. Overall, it is evident that relying solely on a model's performance metric is inadequate. Models with slightly lower performance metrics but significantly higher scores for plausibility and faithfulness may be preferable depending on the specific task. Therefore, the presence of hate speech concealed within sarcasm could augment a model's ability to provide more interpretable outcomes.