## 4 Experiment and Analysis
### Results and Discussions  
Table 3 presents the performance results on the Malay language test dataset using different models. Except for mBERT (Supervised), all other methods utilize Malay unlabeled samples as training data. Results show that LR and SVM methods, which directly extract features from target language unlabeled samples without employing transfer learning techniques, perform the worst. While ML-based classifiers have the advantages of simplicity, intuitiveness, and ease of implementation, they struggle to capture complex semantics and context relationships, lacking support for transfer learning and adaptability to language and task variations. mBERT (Unsupervised), with its multilingual transfer capabilities and ability to train without target language labels, captures shared features to some extent. However, it fails to address issues such as language differences, domain disparities, and label bias. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Models** & **Precision/\%** & **Recall/\%** & **F1/\%** & **BS** & **LL** \\ \hline LR & 52.1 & 40.2 & 33.6 & 0.49 & 0.82 \\ SVM & 62.1 & 50.9 & 44.9 & 0.44 & 0.78 \\ mBERT (Unsupervised) & 60.2 & 52.9 & 48.7 & 0.40 & 0.66 \\ MMSE [66] & 73.5 & 71.4 & 69.4 & 0.28 & 0.47 \\ MDD-VAT [36] & 76.7 & 74.4 & 72.3 & 0.25 & 0.42 \\ Proposed method & 81.5 & 80.5 & 80.7 & 0.14 & 0.23 \\ mBERT (Supervised) & 80.6 & 79.4 & 79.1 & 0.16 & 0.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: OLD performance with different models (/%). The proposed method outperforms other unsupervised approaches in all metrics. This is attributed to using mBERT as the base language model in a dual-branch CLT framework. By minimizing Sinkhorn distance adversarially, the model extracts more effective domain-shared features, enhancing knowledge transfer in cross-language tasks. Additionally, constructing auxiliary OLD tasks aids the model in learning more universal and general offensive text features. The strategy of hiding offensive pivots in the training data forces the model to rely more on contextual information to determine text offensiveness. This approach helps the model better adapt to text features in the target language. The proposed method's superior performance stems from these considerations in addressing challenges like language differences and complex semantics. In contrast to the supervised approach mBERT (Supervised), which excels in adapting to OLD tasks in the target language by utilizing labeled data, it requires a substantial amount of labeled data for effective training. This can be particularly challenging for low-resource languages where obtaining sufficient labeled data is a hurdle. Therefore, mBERT (Supervised) may struggle to adapt to the specific contexts and expressions of offensive language in low-resource languages when faced with insufficient labeled data. In contrast, our proposed method employs a transfer learning framework, performing zero-shot transfer from a high-resource language to a low-resource language, enabling unsupervised offensive text detection. This reduces the reliance on extensive labeled data. By hiding offensive pivots in the second branch of the model and constructing auxiliary OLD tasks, our method compels the model to depend more on contextual information to assess text offensiveness. This aids in learning more universal and general offensive text features. Results indicate that our method's performance in Malay OLD tasks is slightly superior to mBERT (Supervised). In order to further assess the proposed model's zero-shot transfer learning capability for OLD tasks in different languages, we conducted experiments using English as the annotated source language and Danish and Arabic from the 2020 OffensEval shared task 12 dataset as unlabeled target languages. The results, illustrated in Figure 4, demonstrate the model's performance in unsupervised OLD across different languages.