## 7 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Accuracy**} & \multicolumn{3}{c}{**Weighted**} & \multicolumn{3}{c}{**Macro**} & \multicolumn{3}{c}{**Micro**} \\ \cline{3-11}  & & & **F1** & **P** & **R** & **F1** & **P** & **R** & **F1** & **P** & **R** \\ \hline Zero-Shot & \multirow{2}{*}{\
\begin{tabular}{c} XLM-RoBERTa- \\ Classification \\ \end{tabular} } & \multirow{2}{*}{24.54} & \multirow{2}{*}{21.2} & \multirow{2}{*}{30.72} & \multirow{2}{*}{24.54} & \multirow{2}{*}{22.24} & \multirow{2}{*}{31.41} & \multirow{2}{*}{26.99} & \multirow{2}{*}{24.54} & \multirow{2}{*}{24.54} & \multirow{2}{*}{24.54} \\  & large-xnli model. & & & & & & & & & \\ \hline \multirow{2}{*}{Machine Learning} & Nive Bayes (NB) & 58.66 & 58.56 & 59.92 & 58.66 & 57.01 & 58.66 & 56.72 & 58.66 & 58.66 & 58.66 \\  & Logistic Regression (LR) & 57.85 & 58.22 & 59.96 & 57.85 & 56.81 & 57.55 & 57.01 & 57.85 & 57.85 & 57.85 \\ \hline \multirow{2}{*}{Few-Shot Learning} & Data Augmentation & 56.91 & 56.75 & 56.12 & 56.91 & 55.75 & 57.01 & 55.13 & 56.91 & 56.91 & 56.91 \\  & Embedding Lookup & 49.79 & 49.65 & 50 & 49.79 & 48.72 & 49.91 & 48.09 & 49.79 & 49.79 & 49.79 \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Transfer \\ Learning \\ \end{tabular} } & BERT & 69.02 & 69.08 & 71.02 & 69.02 & 68.53 & 68.89 & 69.5 & 69.02 & 69.02 & 69.02 \\ \hline \multicolumn{2}{c}{Our Proposed Approach} & **72.28** & **72.12** & **72.81** & **72.28** & **70.63** & **72.47** & **69.83** & **72.28** & **72.28** & **72.28** \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Performance results of models on the ArPanEmo test set when trained using 21K labeled data (automatically labeled using self-training with majority voting). average F1-scores of our proposed approach and the other six base models during training with varying numbers of labeled examples. Figure 4 illustrates the relationships between the number of training samples and the models' performance when tested on the AETD dataset. From Figure 4, we can observe that the macro, micro, and weighted F1-scores all show improvements as the number of training examples increases. On the small labeled dataset, we noticed that the zero-shot model outperformed other methods, such as few-shot techniques and fine-tuned BERT-based model. However, as the number of training examples increased, the fine-tuned BERT model began to achieve better results than the zero-shot model and other models. ML algorithms and few-shot models performed similarly, and their performance improved with more training examples. However, this improvement was less than that seen in the BERT-based model, especially when the number of training examples reached thousands of labeled examples instead of just hundreds. In general, some models show strong performance with only a few labeled examples, such as the zero-shot model, while others show their strengths with larger number of labeled examples, such as the BERT-based model. Our proposed method performs the best among all the other methods, leveraging the diverse strengths of its base models at both the initial stage of training when data are limited and at the advanced stage when a large number of labeled training data becomes available. Figure 5 shows the relationships between the number of training examples and the performance of various models when tested on the ArPanEmo dataset. Similar trends to those observed on the AETD dataset are also generally observed when using the ArPanEmo dataset, as depicted in Figure 5. We can observe that all models show improvement as the number of training examples increases. However, the extent of improvement varies depending on the model type. When starting with a small number of training examples (specifically, 10 examples per class from the ArPanEmo dataset), the zero-shot model emerges as the second-best performing model after our proposed  
Figure 4: No. of training examples vs. F1-scores when tested on AETD. model.