# Edge Convolutional Networks for Style Change Detection in Arabic Multi-Authored Text
## 5 ECNN-ASCD Evaluation
### Results and Discussion
#### 5.2.1 Hyperparameter Tuning
Third, mBERT ([[https://huggingface.co/google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased)]([https://huggingface.co/google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased)), accessed on 30 April 2025) is a multilingual base model trained on Wikipedia pages of 104 languages, including Arabic, that have the largest content volume on the platform. Fourth, CamelBERT ([[https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-msa](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-msa)]([https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-msa](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-msa)), accessed on 30 April 2025) is based on BERT for Modern Standard Arabic. Fifth, AraBERT is based on BERT architecture and uses the same BERT-Base configuration. In this work, all the models were developed under the same setting. All the trainable parameters of the pretrained models were frozen. Thus, no fine-tuning was performed on their parameters for extracting embeddings. This freezing allowed us to assess the models' capabilities rigorously within the constraints of our experimental setup. The maximum available length for the pretrained models is 256 tokens, which may be less than the length of a single paragraph. Therefore, any paragraph exceeding this length will be truncated using the default truncation strategy, called \"longest-first\". It truncates a token at a time from the longest sequence to reach 256 tokens. A special classification token [CLS] was used to represent paragraph-level classification instead of using the whole input tokens [61]. Table 6 shows the results of the fine-tuning experiments. Both the CamelBERT-based and AraBERT-based models yielded high results, where they were trained especially on Arabic texts based on the BERT \begin{table}
\begin{tabular}{c|c c c|c c c} \hline \hline \multirow{3}{*}{Layer} & 2 & AraBERT & 0.001 & 20 & 0.9868 & 0.9004 & 0.8332 \\  & 3 & AraBERT & 0.001 & 20 & 0.9911 & 0.9199 & 0.8536 \\  & 4 & AraBERT & 0.001 & 20 & **0.9918** & 0.9214 & **0.8684** \\  & 5 & AraBERT & 0.001 & 20 & 0.9915 & **0.9236** & 0.8663 \\ \hline \multirow{3}{*}{Encoder} & 4 & RoBERTa-Davlan & 0.001 & 20 & 0.9679 & 0.8693 & 0.7795 \\  & 4 & [PERSON] & 0.001 & 20 & 0.9883 & 0.8782 & 0.7726 \\  & 4 & [PERSON] & 0.001 & 20 & 0.9516 & 0.8354 & 0.4935 \\  & 4 & [PERSON] & 0.001 & 20 & 0.9903 & 0.9179 & 0.8578 \\  & 4 & **[PERSON]** & 0.001 & 20 & **0.9918** & **0.9214** & **0.8684** \\ \hline \multirow{3}{*}{LR} & 4 & [PERSON] & 0.0002 & 20 & 0.98260 & 0.90487 & 0.84113 \\  & 4 & [PERSON] & **0.001** & 20 & **0.9918** & **0.9214** & **0.8684** \\ \hline \multirow{3}{*}{Epoch} & 4 & [PERSON] & 0.001 & 20 & 0.9918 & 0.9214 & 0.8684 \\  & 4 & [PERSON] & 0.001 & **50** & **0.9945** & **0.9381** & **0.9120** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Hyperparameter tuning (highest scores and corresponding hyperparameter values in bold). architecture. In particular, the superior performance of AraBERT can be attributed to its ability to capture the author's style. Thus, style patterns extracted by AraBERT are more effective in representing and learning. Adjusting the learning rate (LR) also plays a crucial role in optimizing model performance. Previous SCD works utilized a large range of LR values, such as 0.001 [55] and 0.00002 [81; 95; 98; 99; 100]. Several experiments were conducted using the ADAM optimizer [101] with different LR values. Table 6 reports two values. Small LR values can lead to slower convergence with more stability due to making more minor updates at each iteration. Large LR values may help achieve fast convergence but risk overshooting the optimal value. According to the results, the LR of 0.001 yielded better results and strikes a balance between rapid convergence and stable training. Regarding the epoch, training ML-based models aims to learn them till reaching convergence.