# Edge Convolutional Networks for Style Change Detection in Arabic Multi-Authored Text
## 5 ECNN-ASCD Evaluation
### Results and Discussion
#### 5.2.2 Ablation Experiments  
Several ablation experiments were conducted to prove the effectiveness of the main components in ECNN-ASCD. The experiments involved cumulatively adding components up to obtaining the proposed solution. The \(F_{1}\) results of these experiments are summarized in Table 7. Each row represents a model that contains a component mentioned in the same row and all the added prior ones. The table showcases the \(F_{1}\) scores for three levels of instances with paired \(t\)-test results. The subscript values \"\(\pm\)0.\(X\)\" represent the difference between the current model score and the one directly above it. The symbols \"S\" and \"NS\" denote that there is a statistically significant difference and insignificance, respectively, after adding the current component. The symbol \"\(\Leftarrow\)\" indicates that the model containing the current component surpasses its predecessor. Conversely, the symbol \"\(\dagger\)\" signifies the prior model's dominance before introducing the current component. The last column shows the final decision, either adding or removing the current component according to the provided influence. The six main components were studied, which are GNN modules, a warmup mechanism, previous edge representations, initial node representations, an attention mechanism, and the convergent case. The components are discussed in detail below. Figure 4: F\({}_{1}\) scores of ECNN-ASCD across epochs from 1 to 50 on different instances: (**a**) easy instances; (**b**) medium instances; and (**c**) hard instances. First, the base component is an appropriate GNN module, either GCN or EdgeConv. These models represent the baseline models without any additional components. Specifically, each model uses four GNN layers, either GCN or EdgeConv, where the layer count was determined during the tuning phase. The nodes in path graphs represent paragraphs. Each edge was labeled to indicate whether there is a style change between end nodes. Edge representations were classified by summing the representations of two end nodes extracted from the fourth GNN layer. As can be seen, basic EdgeConv statistically outperforms basic GCN significantly on two difficulty levels. It shows that EdgeConv enhanced the learning of edge representations for SCD better than the GCN-based model. Second, the warmup mechanism was added to optimize the models. This mechanism helps models to train with a very low LR and then it will be linearly increased until reaching the global minimum of the loss function. [PERSON] et al. (2018) noted that their SCD models have minimum convergence levels. Introducing the warmup with the ratio of 0.1 enhanced their models significantly. In this study, the same ratio is used. As a result, including the warmup mechanism significantly improves performance across all difficulty levels. Third, edge representations obtained from the previous model are static representations. They do not directly benefit from using deep layers. Thus, adding edge representations from a prior layer into a current layer enables continual learning and adjustment across layers. The incorporation also prevents edge representations from relying only on node representations from the last layers. Table 7 showcases the significant improvement after adopting dynamic edge representations. [PERSON] and [PERSON] (2018) and [PERSON] et al. (2018) adjusted edge representations across layers within their GNN-based models. The best performance was achieved when they used their proposed edge representation approach. Fourth, initial node information is aggregated while measuring edge representations. As shown in Table 7, the fusion of both initial node and edge representations can mitigate the over-smoothing issue and contribute to further performance gains. [PERSON] et al. (2018) developed a deep GNN architecture with 64 layers, and they obtained better results when aggregating the initial node representations.