## 1 Introduction
In the English context, ample high-quality annotated data facilitates effective DL model training. However, in the Malay language scenario, there is a lack of publicly available large-scale datasets for offensive comments, posing challenges for training DL models. Cross-lingual transfer (CLT) technique seeks to address the aforementioned challenge by transferring knowledge across languages, enabling models to accomplish tasks in a low-resourced target language with annotated samples from another source language [13]. The scenario of interest is zero-shot CLT, where the target language lacks any annotated samples [14]. This constitutes the most common and challenging issue in CLT research, and it is the focus of this paper. Specifically, our attention is on enhancing the performance of OLD task in zero-shot CLT scenarios. Recent findings highlight the remarkable CLTcapabilities of Multilingual BERT (mBERT), a model pre-trained on Wikipedia data in 104 languages. Fine-tuning mBERT using annotated samples from the source language has emerged as a mainstream approach for cross-lingual text label prediction [15]. Given the practical scenario where unlabeled samples in the Malay language are easily obtainable and contain relevant information about the target distribution, we propose that jointly utilizing annotated samples from the source language (English) and unlabeled samples from the target language (Malay) for fine-tuning mBERT is an effective strategy to enhance cross-lingual OLD performance. Detecting offensive speech in low-resource languages faces two primary challenges. Firstly, the limited availability of resources hampers the effective semantic encoding of text in these languages. Secondly, it impedes the effective training on offensive features inherent to these languages. Currently, most approaches to detecting offensive speech in low-resource languages rely on cross-lingual, pre-trained models. The foremost advantage of such methods lies in their ability to leverage unsupervised cross-lingual pre-training, thereby enabling detection in low-resource languages. However, previous methods have often overlooked the linguistic disparities between the source and target domains during transfer learning, consequently undermining detection performance in the target domain. Building on the analysis above, this paper proposes a dual-branch CLT framework based on mBERT, designed to facilitate zero-shot transfer learning from high-resource languages (e.g., English) to low-resource languages (e.g., Malay), thereby enhancing the detection of offensive speech in low-resource languages. The main contributions of this paper include:  
1. Utilizing the first network branch of the mBERT model to extract features from both source and target domain data. By adversarially minimizing the Sinkhorn distance, effective domain-shared features are obtained. 2. Identifying offensive pivot words through an unsupervised model and removing these pivots from the training data of the second network branch, thereby constructing an auxiliary offensive speech detection task. This encourages the model to rely on contextual information, enhancing detection performance in low-resource languages. 3. Constructing a Malay offensive speech detection dataset, analyzing the knowledge graph of various offensive pivots, and introducing statements with or without pivot words to assess the model's understanding of deep semantics and its applicability in real-world scenarios. The structure of the remainder of this paper is as follows. Section 2 reviews related research on OLD filed, including traditional manual feature detection methods, machine learning approaches, and deep learning techniques. Section 3 provides a detailed description of the proposed dual-branch zero-shot cross-lingual OLD framework based on Sinkhorn distance and pivots extraction. Section 4 validates the effectiveness of the proposed method through extensive experiments on low-resource Malay text for offensive speech detection.