## 4 Experiment and Analysis
### Results and Discussions
Moreover, in transfer learning, certain pivot words that are offensive in the source language may not carry the same meaning in the target language, leading to misjudgments. For example, in the instance S3 from Table 4: \"Sejujurnya, saya baru mencuba respi misakan baru, tetapi rasa yang dihasilkan agak bodoh. Mungkin saya perlu menambah lebih rempah pada masa akan datang!\" (\"To be honest, I just tried a new recipe, but the taste it produced was a bit bland. Maybe I need to add more spices in the future!\"), the use of the word \"Bodoh\" is to express a bland taste rather than for offensive purposes. In Malay, \"Bodoh\" is sometimes used to describe something's underperformance but usually without insult. Similarly, some sentences may lack offensive words but still be aggressive using sarcasm or mockery, as seen in comment S4 from Table 4: \"Intelek kamn nampaknya hanya mencukupi untuk melakukan perkara yang mudah, seperti bernasa\". (\"Your intelligence seems to be sufficient only for doing simple things, like breathing\"). This statement is highly offensive as it includes derogatory remarks about the other person's intelligence. Aggression is not solely about specific words but also encompasses the overall tone and intent of the language. When constructing a model for detecting offensive text, one must consider such indirect forms of aggression. In our experiments, the comparative methods failed to correctly identify the offensiveness of the examples S3 and S4. In contrast, our proposed method successfully determined the offensiveness of these sentences through the joint training strategy of hiding pivot words and the domain-specific feature extraction capability in the auxiliary OLD task. Finally, we analyze the impact of varying training resources on the transfer effect. We take different quantities of labeled training data from the source language (English), including 1000, 2000, 5000, and all (13,240) samples, and examine the performance of the proposed method on the Malay dataset. Figure 6 presents the F1 results, maintaining the training and testing data volume of non-tabled Malay samples consistent with the previous experiments. The results reveal that even with just 1000 source language training samples, the proposed method's performance surpasses the earlier experiment's direct transfer of the mBERT (Unsupervised) model. This demonstrates the method's high sample utilization efficiency. A larger quantity of labeled source language data implies a richer knowledge base for the model. This knowledge can be effectively transferred to the target language in transfer learning, providing a better starting point and helping the model adapt to the target language's context and expressions. Abundant source language data contributes to establishing more robust and universal feature representations, crucial for OLD models, as offensive language might exhibit similar features across different languages. In summary, the proposed model leveraging annotated samples from high-resource languages can achieve more robust and accurate OLD in the target language, providing substantial support for handling low-resource language data. In order to further evaluate the effectiveness of each component module proposed in this study, we conducted ablation experiments. Figure 7 presents performance results on the Malay test dataset, where all models were trained using annotated English texts and unlabeled Malay texts, with Malay texts as the test data. Model 1 solely employed the unsupervised mBERT model, utilizing its multilingual transfer capabilities directly for detecting offensive text in the target language. Model 2 used only the first network branch of the proposed method (left branch in Figure 1), replacing the SDRL layer with a Gradient Reversal Layer (GRL), a common technique in domain adaptation aimed at reducing feature differences between different domains (such as source and target domains) to enhance model generalization in the target domain.