## 5 Conclusions  
In addressing the shortcomings of existing methods in feature extraction for low-resource languages, such as Malay, in OLD tasks, this paper introduces a dual-branch zero-shot cross-lingual model based on mBERT. Effectively leveraging the Sinkhorn distance, the model acquires cross-lingual shared features with strong discriminatory power for detecting offensive content. Augmented with an auxiliary label prediction task, unique domain-specific features are extracted. The combination of these features facilitates robust training and achieves superior performance in offensive text detection compared to existing methods. The success of this approach can be attributed to the comprehensive utilization of the multilingual pre-trained model mBERT, efficient feature extraction strategies, adept adversarial training, and a thoughtful approach to handling offensive pivot words and dataset construction. Collectively, these factors contribute to the outstanding performance of this method in unsupervised CLT for offensive text detection.  
Moreover, the proposed method may also face certain limitations. The interpretability of the adopted mBERT model is notably poor, restricting the model's credibility and explainability in specific scenarios. Despite employing unsupervised learning methods, the construction of datasets for OLD still relies on labeled data from high-resource languages such as English. The process of constructing and handling offensive pivot words could potentially render the model sensitive to specific attributes within the dataset. In our future research endeavors, we aim to explore the development and application of visualization tools such as LIME (Local Interpretable Model-agnostic Explanations) or SHAP (SHapley Additive exPlanations) to aid in elucidating the model's decision-making processes and feature importance. Additionally, we intend to investigate active learning strategies to automatically select the most informative samples for annotation, thereby reducing dependency on extensive annotated data from the source language. Furthermore, by introducing more variations and noise from real-world scenarios into our datasets, we seek to mitigate the model's reliance on specific data attributes.  
In summary, the proposed approach demonstrates significant advancements in addressing OLD challenges in low-resource languages, providing valuable insights for the application of CLT learning in safeguarding security and order in social media environments. As further improvements in model performance and generalization are pursued, the potential impact of this method in practical applications is expected to grow. Future research endeavors will explore the application of this approach to various tasks, including machine translation, text generation, and other areas within the field of NLP.  
Conceptualization, X.G., H.M.A. and M.Z.Z.A.; Methodology, X.G., H.M.A. and M.Z.Z.A.; Software, X.G.; Validation, X.G., H.M.A. and M.Z.Z.A.; Investigation, X.G.; Resources, X.G. and M.Z.Z.A.; Writing--original draft, X.G.; Writing--review & editing, H.M.A. and M.Z.Z.A.; Supervision, H.M.A. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the data are part of an ongoing study. Conflicts of Interest: The authors declare no conflict of interest.