# Enhancing Propaganda Detection in Arabic News Context Through Multi-Task Learning
## 4 Experiments
### Experimental Setup  
All experiments were conducted on a single NVIDIA Tesla T4 GPU equipped with 15 GB of memory and utilizing Google Colab Pro+ (a robust Python 3.11 programming environment). As in [23], the dataset was split into 75%, 8.5%, and 16.5% for training, development, and testing, respectively. Transformer toolkits were employed to develop a pipeline for propaganda, sentiment, and emotion analysis [55]. For the PLM, AraBERT served as the backbone model for training on the MultiProSE dataset, which encoded both news text and labels as hidden representations. This model was selected because of its SOTA performance, as demonstrated in [22]. The proposed system initiated the preprocessing of Arabic texts, as described in Section 3. Based on the training data visualization, the maximum sequence length was set to 256 tokens. After conducting multiple empirical experiments, the batch size and number of epochs were set to eight and five, respectively. An AdamW optimizer was used with a learning rate of \(2\times 10^{-5}\)[56]. The hyperparameter, known as \"patience,\" was set to five, which represented the maximum number of epochs that could elapse without observable improvement, at which point the training process was terminated. To prevent overfitting, the weight decay was set to \(1\times 10^{-5}\). Additionally, for each experiment, five runs with distinct random seeds were performed, and the average performance over the test subset was reported. Furthermore, to optimize the performance of the proposed models, an exhaustive grid search algorithm to tune the hyperparameters of every task-weighting scheme was employed. A grid search enumerates the Cartesian product of predefined value sets, trains a model for each resulting combination, and retains the configuration that maximizes performance on the development split [57]. Specifically, the grid search was conducted based on the evaluation of the macro F1 score on the development set, with the optimal hyperparameters selected accordingly for each model. Then, the configuration that achieved the highest macro-F1 on the development set was subsequently retrained and evaluated on the held-out test set. For example, in the Hierarchical Weighting scheme, the search space was defined as \(\lambda\in\{0.1,\,0.5,\,1\}\), upper bound \(\in\{0.5,\,1,\,2\}\), and lower bound \(\in\{0.001,\,0.01,\,1\}\), yielding \(3\times 3\times 3=27\) configurations. Each \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model Categories** & **Model Name** & **Model Description** \\ \hline \multirow{8}{*}{Proposed Task-Weighting} & MTL-DDW & An MTL setting that utilizes dynamic \\  & & difficulty weighting. \\ \cline{2-3}  & MTL-PGRW (NormalDist) & An MTL setting that utilizes priority-guided random \\  & & weighting, based on a normal sampling distribution. \\ \cline{2-3}  & MTL-PGRW (BernoulliDist) & An MTL setting that utilizes priority-guided random \\  & & weighting, based on a Bernoulli sampling distribution. \\ \cline{2-3}  & MTL-PGRW (UniformDist) & An MTL setting that utilizes priority-guided random \\  & & weighting, based on a uniform sampling distribution. \\ \cline{2-3}  & MTL-HW & An MTL setting that utilizes hierarchical weighting. \\ \cline{2-3}  & MTL-UW & An MTL setting that utilizes uncertainty weighting. \\ \cline{2-3}  & MTL-PASUW & An MTL setting that utilizes priority-aware \\  & & softmax-based uncertainty weighting. \\ \hline \multirow{4}{*}{MTL Models with the Best Proposed Task-Weighting} & MTL-Sent-UW & An MTL model that leverages two tasks, propaganda \\  & (PSMTL-UW) & and sentiment, and utilizes uncertainty weighting. \\ \cline{2-3}  & MTL-Emo-UW & An MTL model that leverages two tasks, propaganda \\ \cline{1-1}  & (PEMTL-UW) & and emotion, and utilizes uncertainty weighting \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._configuration was trained and validated under the same fixed schedule to isolate the influence of the weighting parameters.