## 4 Experiment and Analysis
### Results and Discussions
Compared to the results in Malay, it is evident that despite having the fewest unlabeled training samples in the target language and the most in Arabic, the proposed method performs best on the Danish dataset and least effectively on Arabic. This discrepancy arises due to the significant impact of semantic similarity on transfer learning efficacy. The closer the target language is to the source language, the better the knowledge transfer. Danish and English share some commonalities as they both belong to the Germanic language family, exhibiting certain similarities in grammar, vocabulary, and language structure. On the other hand, Arabic and Malay belong to different language families, showing greater differences from English in terms of grammar and structure. Additionally, Arabic is a right-to-left written language, while English is left-to-right, further increasing the dissimilarity between them. Malaysia, being a culturally diverse country with Malay, Chinese, Indian, and other ethnic groups, has a unique multicultural influence shaped by neighboring countries, history, and religion. In contrast, Arabic countries predominantly follow Islamic culture. The cultural disparities between Arabic-speaking countries and English-speaking ones may contribute to the decrease in model transfer learning performance. The proposed method incorporates pivot extraction and hiding strategies in the training data, utilizing a second network branch for auxiliary label prediction tasks to assist the model in capturing language-specific features of offensive speech. Offensive pivots play a crucial role in OLD tasks. These pivots are key terms in the text that often carry malicious, insulting, or hostile semantics. By detecting and analyzing these pivot words, the model can more accurately identify and classify offensive speech. Therefore, the presence of pivot words can serve as one of the bases for text classification by the model. Figure 5 illustrates the distribution of offensive pivots in the Malay language dataset. Table 4 provides details on pivot extraction in the dataset along with some examples. The presence of aggressive pivot words may indicate that the entire text is more likely to be offensive. For instance, in the examples S1 and S2 from Table 4, the model can determine both sentences as offensive using pivots like \"bangsat\", \"penipuan\", and \"penipu\". However, the appearance of aggressive pivot words might introduce noise, leading the \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Sample Number** & **Sentence** & **Pivots** & **Offensive?** \\ \hline S1 & & Kau ni tak guna, macam bangsat. & bangsat & yes \\  & & (You are useless, like scum.) & \\  & & Laman web and adalah satu & \\  & & penipuan. Jangan percayakan & \\ S2 & & penipu ini. & \\  & & (Your website is a scam. Do not & penipu & yes \\  & & trust this exammer.) & \\  & & Dia buat perkara yang agak & \\  & & bodoh tetapi sebenamya dia & \\ S3 & & punya hati yang baik. & Bodoh & no \\  & & (He did something rather foolish, & \\  & & but actually he has a good heart.) & \\  & & Kebijaksanaammu nampakanya & \\  & & hanya mencukip unituk & \\  & & melakukan perkara-perkara & \\ S4 & & mudah, seperti bernafas. & / & yes \\  & & (Your intelligence seems to be & \\  & & sufficient only for doing simple & \\  & & things, like breathing.) & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Instances from the collected dataset and pivot extraction examples. Figure 4: Zero-shot OLD results of the proposed method across different languages. Figure 5: Visualization pivots distribution. model to misinterpret some sentences. Some texts contain aggressive pivot words, but the overall context is not offensive, resulting in false positives. Some offensive statements may lack common aggressive pivot words, causing the model to miss them. Additionally, some texts may not contain aggressive pivot words but still possess offensive content. Overreliance on pivot words may cause the model to overlook such cases.