# RTACompensator: Leveraging AraBERT and XGBoost for Automated Road Accident Compensation
## 4 Research Methodology
### Analysis and Discussion
This performance difference highlights the effectiveness of Gemini's data augmentation strategy, which generates more diverse and contextually rich training examples, enhancing the model's ability to generalize and handle complex accident descriptions. While both models exhibit high accuracy and reliability, the Gemini-augmented approach further strengthens the robustness of the proposed method for automating responsibility determination in real-world scenarios. The comparative results in Figure 17 clearly illustrate our model's category-wise performance in determining responsibility for road accidents using datasets augmented with Gemini and multilingual BERT. The first Figure 17a reveals that categories with larger document sets and higher average word counts, such as 1/3, 1/4, and 1/5, achieve near-perfect or perfect precision, with Gemini augmentation reaching 100% precision. This highlights the advantage of Gemini's augmentation strategy, which produces longer documents enriched with contextual details. Notably, the 1/4 category, with the highest word count in the dataset, achieves perfect precision, recall, f1-score, and accuracy. Similarly, the 1/3 and 1/5 categories benefit from the data's richness, resulting in near-perfect performance across all metrics. Figure 17b (recall comparison) shows a similar trend. The recall values are consistently high across categories for both augmentations, with Gemini showing slightly superior performance in the most complex categories. Categories with shorter average document  
lengths, such as 2/3 and 3/4, exhibit slightly lower recall values due to the limited contextual information available. This observation underscores the importance of textual richness in enabling the model to generalize effectively. Figure 17c (f1-score comparison) reinforces the dominance of Gemini-augmented data. The f1-scores for categories such as 1/3, 1/4, and 1/5 reach 100%, outperforming those achieved with multilingual BERT augmentation. However, categories with fewer words per document, such as 3/4 and 2/3, display slightly reduced f1-scores, reflecting the model's challenges in processing limited textual data. Notably, Gemini's augmentation mitigates this limitation by enhancing contextual richness. Finally, Figure 17d (accuracy comparison) demonstrates the robustness of the model across all categories, with Gemini consistently achieving higher or equal accuracy compared with multilingual BERT. Categories with abundant data, such as 1/4 and 1/5, achieve 100% accuracy with Gemini augmentation, showcasing the benefits of the additional contextual depth. Even in challenging categories like 3/4, where the document sets are smaller, Gemini maintains a competitive accuracy of 93.7%, outperforming BERT. These findings highlight the clear advantage of Gemini's augmentation technique, which excels in generating richer, more detailed textual data. By providing longer and more contextually nuanced documents, Gemini enables the model to achieve superior classification performance across all metrics. This comparative analysis underscores the importance of data augmentation strategies in enhancing model performance, particularly for complex tasks such as assigning responsibility in road accidents. The results of the additional experiment on the Khaleej dataset (Table 7) demonstrate the robustness of our responsibility determination model. Using a dataset of 6500 documents across diverse categories including sports, culture, medicine, technology, politics, finance, and religion, the model achieved an impressive accuracy of 97.81%, outperforming the CGRU model (96.0%) and DL ArCAR (97.47%). This superior performance highlights the model's efficacy and its ability to generalize across varied content and categories. Its consistent accuracy across different document types further validates its adaptability and reliability on diverse datasets.