## 6 Experiments
### Low-Resource Scenarios (RQ3)
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Accuracy**} & \multicolumn{4}{c}{**Weighted**} & \multicolumn{4}{c}{**Macro**} & \multicolumn{4}{c}{**Micro**} \\ \cline{3-13}  & & & **F1** & **P** & **R** & **F1** & **P** & **R** & **F1** & **P** & **R** \\ \hline Zero-Shot & \multirow{2}{*}{\begin{tabular}{c} XLM-RoBERTa- \\ Classification \\ \end{tabular} } & \multirow{2}{*}{34.03} & \multirow{2}{*}{30.93} & \multirow{2}{*}{42.67} & \multirow{2}{*}{34.03} & \multirow{2}{*}{32.44} & \multirow{2}{*}{42.6} & \multirow{2}{*}{37.06} & \multirow{2}{*}{34.03} & \multirow{2}{*}{34.03} & \multirow{2}{*}{34.03} & \multirow{2}{*}{34.03} \\  & & & & & & & & & & \\ \hline \multirow{2}{*}{Machine Learning} & \multirow{2}{*}{\begin{tabular}{c} Nive Bayes (NB) \\ Logistic Regression (LR) \\ \end{tabular} } & 69.67 & 68.94 & 69.8 & 69.67 & 68.84 & 70.44 & 68.76 & 69.67 & 69.67 & 69.67 \\  & & 66.1 & 63.63 & 63.65 & 66.09 & 63.63 & 68.76 & 64.9 & 66.1 & 66.1 & 66.1 \\ \hline \multirow{2}{*}{Few-Shot Learning} & \multirow{2}{*}{\
\begin{tabular}{c} Data Augmentation \\ Embedding Lookup \\ \end{tabular} } & 65.7 & 63.56 & 64.82 & 65.7 & 63.46 & 65.26 & 64.87 & 65.7 & 65.7 & 65.7 & 65.7 \\  & & 58.69 & 58.28 & 58.04 & 58.69 & 58.18 & 58.04 & 58.49 & 58.69 & 58.69 & 58.69 \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Transfer \\ Learning \\ \end{tabular} } & \multirow{2}{*}{BERT} & 81.65 & 81.66 & 81.73 & 81.86 & 81.86 & 82.01 & 81.78 & 81.65 & 81.65 & 81.65 \\ \hline \multicolumn{2}{c}{Our Proposed Approach} & **83.15** & **83.19** & **83.31** & **83.15** & **83.33** & **83.54** & **83.21** & **83.15** & **83.15** & **83.15** \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Performance results of models on the AETD dataset when trained using 22K labeled data (automatically labeled using self-training with majority voting). \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{4}{c}{**Weighted**} & \multicolumn{4}{c}{**Macro**} & \multicolumn{4}{c}{**Micro**} \\ \cline{3-13}  & & & **F1** & **P** & **R** & **F1** & **P** & **R** & **F1** & **P** & **R** \\ \hline \multirow{2}{*}{\begin{tabular}{c} Zero-Shot \\ Classification \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} XLM-RoBERTa- \\ large-xnli model. \\ \end{tabular} } & \multirow{2}{*}{26.36} & \multirow{2}{*}{20.07} & \multirow{2}{*}{22.84} & \multirow{2}{*}{26.36} & \multirow{2}{*}{20.07} & \multirow{2}{*}{22.84} & \multirow{2}{*}{26.36} & \multirow{2}{*}{26.36} & \multirow{2}{*}{26.36} & \multirow{2}{*}{26.36} & \multirow{2}{*}{26.36} \\  & & & & & & & & & & \\ \hline \multirow{2}{*}{Machine Learning} & \multirow{2}{*}{\begin{tabular}{c} Nive Bayes (NB) \\ Logistic Regression (LR) \\ \end{tabular} } & 24.79 & 24.88 & 26.6 & 24.79 & 22.45 & 23.84 & 22.79 & 24.79 & 24.79 & 24.79 \\  & & 24.7 & 24.81 & 26.14 & 24.7 & 22.58 & 23.62 & 22.83 & 24.7 & 24.7 & 24.7 \\ \hline \multirow{2}{*}{Few-Shot Learning} & \multirow{2}{*}{\
\begin{tabular}{c} Data Augmentation \\ Embedding Lookup \\ \end{tabular} } & 25.04 & 24.42 & 26.03 & 25.04 & 22.23 & 25.39 & 21.94 & 25.04 & 25.04 & 25.04 & 25.04 \\  & & 22.65 & 22.5 & 22.82 & 22.65 & 21.37 & 22.64 & 20.68 & 22.65 & 22.65 & 22.65 \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Transfer \\ Learning \\ \end{tabular} } & \multirow{2}{*}{BERT} & 14.03 & 9.15 & 8.95 & 14.03 & 7.45 & 7.5 & 11.36 & 14.03 & 14.03 & 14.03 \\ \hline \multicolumn{2}{c}{Our Proposed Approach} & **31.53** & **31.24** & **31.47** & **31.53** & **29.41** & **30.16** & **29.33** & **31.53** & **31.53** & **31.53** \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Performance results of models on ArPanEmo test set when trained using a small amount of labeled data (110 examples from ArPanEmo). The second-best performing model was the fine-tuned BERT-based model on the 21K examples.