# Edge Convolutional Networks for Style Change Detection in Arabic Multi-Authored Text
## 5 ECNN-ASCD Evaluation
### Experiment Settings  
The same settings were used for all the experiments. They are identified from different aspects and described in this subsection, including the datasets, models for comparison, setup of the environment, techniques, and evaluation metrics. **Datasets:** AraSCD ([[https://github.com/abeersaad0/SCD/tree/main](https://github.com/abeersaad0/SCD/tree/main)]([https://github.com/abeersaad0/SCD/tree/main](https://github.com/abeersaad0/SCD/tree/main)), accessed on 30 April 2025) is a large Arabic dataset for SCD. It holds 30,000 documents extracted from several publicly available Arabic linguistic resources. It contains three classes of instances according to difficulty level: hard, medium, and easy. The criteria for this classification focus on categories of texts and the time period during which these texts were written. AraSCD encompasses three categories of text, poetry, books, and newspapers, which exhibit diverse author backgrounds and may enrich AraSCD with varying writing styles. Hard instances were written by five poets during a single era; medium instances were authored by both three poets of the same age and two writers who wrote two books on a similar domain; and easy instances encompass texts written by two poets from two different eras, two writers of two books covering two distinct topics, and one newspaper writer. Table 4 provides an overview of its statistics. Each level has the same number of documents. The average lengths of documents and paragraphs are measured as the average number of paragraphs and words, respectively, per document. The average number of style changes is measured per document. The percentage of style changes signifies the ratio of changes relative to all boundaries in the dataset. **Baseline models:** Two baseline models were evaluated on each test set in AraSCD. First, Baseline-Predicting 0 (Baseline-Pr0) assigns the value 0 to all predicted labels, implying no style changes across all boundaries in the test sets. Hence, this baseline model predicts that all documents are single-authored. Second, Baseline-Predicting 1 (Baseline-Pr1), in contrast, assigns the value 1 to all predicted labels, suggesting that changes in writing styles occur across all test set boundaries. As a result, this baseline model suggests that all documents are written by multiple authors. **Other models for comparison:** Since no prior Arabic work had been conducted, two models were developed in this study for comparison purposes with the results of ECNN-ASCD. First, a basic machine learning model named BERT-MLP was developed. It encodes the input using AraBERTv02 ([[https://huggingface.co/aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02)]([https://huggingface.co/aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02)), accessed on 30 April 2025), which is the same selected pre-trained model for ECNN-ASCD. BERT-MLP classifies the input based on two FC layers with 128 neurons. It is worth noting that the classification in the FC layers maps one sample in the input with one predicted output. Thus, a sample for BERT-MLP is a boundary represented by concatenating two sequential paragraphs separated by a [SEP] token. Second, GCNN-ASCD was developed based on the GCN module. For a fair comparison, GCNN-ASCD maintained the same ECNN-ASCD architecture except for replacing the EdgeConv module with the GCN module in all the layers. **Environment setup:** All the experiments were run on a computer with an Intel(R) i7 processor up to 5.60 GHz, 64-bit, ASUS TUF RTX 4090 24 GB OC GAMIING, and 64 GB (2 \(\times\) 32 GB) DDR5 5600 Mhz. **Technical setup:** Python 3.12 and the PyTorch 2.4 framework were used to develop the models. Each input document is used as a single batch to update the weights, as the test phase in the real scenario is applied to at least one document.