# Enhancing Propaganda Detection in Arabic News Context Through Multi-Task Learning
## 3 Methodology
### Task-Weighting
#### 3.6.3 Task-Weighting Schemes
* \(\alpha\) represents priority-guided parameter. * \(\lambda_{t}\) represents the randomly sampled task weight for task t.
* \(I_{pro}(t)\) represents the indicator function (1 if t = propaganda, and 0 otherwise). * \(w_{t}\) represents the final normalized task weight. * \(j\) represents a variable that iterates through all the tasks in \(T\) when computing the sum (softmax denominator). Hierarchical Weighting (HW)  
The researchers in [53] employed a hierarchical MTL framework with a structured weighting scheme for task prioritization. The model jointly trained three hierarchically related tasks: sentence function identification, paragraph function identification, and organization evaluation. Organizational evaluation was the primary essay-scoring objective. These tasks were organized hierarchically, with higher-level tasks assessing the overall organization of the essay being prioritized over lower-level tasks that examined more granular features such as grammar or coherence. Inspired by this, we employed a dynamic weight assignment strategy that allocated a higher weight to lower-level tasks, such as sentiment and emotion, during the initial phases of training. Then, we progressively shifted the weight toward the target task, which was propaganda in the later stages. The scheme operated under the hypothesis that its optimal performance on the target task requires initial proficiency in foundational tasks because these tasks provide the necessary scaffolding for subsequent learning. For instance, the sentiment task is critical for propaganda detection because a text's propaganda is often closely linked to its sentiment. Equation (20) represents the calculation:  
\[L_{global}=w_{prop}.L_{Pro}+L_{Sen}+L_{Emo} \tag{20}\]  
where the learnable generated weight \(w_{prop}\) is dynamically updated as follows:  
\[P(c_{i})=\text{Sigmoid}(y_{i})=\frac{1}{1+\text{e}^{-y_{i}}} \tag{12}\]0  
The learnable weight, \(w_{prop}\), was employed to regulate the relative importance of \(L_{Pro}\) based on the empirical assumption that \(L_{Sen}\) and \(L_{Emo}\) are of equal significance. Initially, it was set to 0.1. Additionally, \(w_{prop}\) ensured that all tasks were given equal emphasis during the early stages of training. As \(L_{Sen}\) became smaller than \(L_{Pro}\), \(w_{prop}\) gradually shifted, allowing the model to progressively prioritize the propaganda detection task as \(L_{Sen}\) increased. Section 4.3 provides a comprehensive explanation of the selection process for these parameters. 1. UW  
Generally, uncertainty in learning models can be classified into two primary types: epistemic and aleatoric uncertainty. Epistemic uncertainty refers to a model's lack of knowledge owing to limited training data. In contrast, aleatoric uncertainty arises from the inherent variability in the data, which the model cannot explain. Moreover, aleatoric uncertainty can be further divided into two categories: heteroscedastic and homoscedastic uncertainty. Heteroscedastic uncertainty is data dependent, meaning that its variance varies across the input data. In contrast, homoscedastic uncertainty is task dependent, assuming consistent uncertainty across different instances of the same task. Consequently, homoscedastic uncertainty is suitable for tasks with similar difficulty levels, whereas heteroscedastic uncertainty is more appropriate when task difficulties vary, leading to different model performance and uncertainty levels. This approach is based on the principle that tasks exhibiting higher uncertainty should be assigned lower weights, whereas tasks exhibiting lower uncertainty should be assigned higher weights [49]. The authors of [54] used homoscedastic uncertainty as a dynamic weighting mechanism for heterogeneous vision tasks, specifically per-pixel depth regression, semantic segmentation, and instance segmentation within a single convolutional network.