## 2 Related Works
[25] compared Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM) models with traditional ML tools for detecting hate speech on Twitter. The results demonstrated the superior performance of DL models over ML models. [PERSON] et al. [26] conducted a detection analysis of user comments on news portals and Wikipedia, identifying those containing abusive language. Leveraging a Greek news portal, they built a dataset of 1.6 million user comments, employing a Recurrent Neural Network (RNN) with an attention mechanism for classification. The attention mechanism automatically highlights potentially offensive words in comments. [PERSON] et al. [27] combined CharCNN and WordCNN to create a hybrid CNN model for OLD. Considering the uniqueness of Twitter tweets, the model simultaneously learned features at the character and word levels. [PERSON] et al. [28] built a Danish language dataset, employing an LSTM-based method to detect hate speech, cyberbullying, and other offensive language. [PERSON] et al. [29] applied Graph Convolutional Networks (GCN) in OLD tasks. By introducing additional information such as user personal data and social networks to address the limited features of short texts, they input data as graph nodes into GCN for learning. Through graph convolutional networks, the model could directly capture user language behavior and group structure features. Experimental results indicated that the combination of LR and GCN yielded the best results. Recent studies have demonstrated the effectiveness of pre-trained language models like BERT in downstream tasks such as OLD [30]. [PERSON] et al. [31] utilized fine-tuned BERTfor downstream classification tasks to identify offensive tweets. [PERSON] et al. [32] successfully implemented transfer learning in offensive language detection using BERT and the ULMFiT (Universal Language Model Fine-tuning for Text Classification) method. Due to superior performance, such approaches have become mainstream for addressing OLD tasks. In the 2019 OffensEval competition, 7 out of the top 10 teams used BERT, differing mainly in parameter settings and preprocessing steps. As mBERT exhibits great CLT capabilities, it has gradually become the foundation for recent cross-lingual text detection and classification tasks. [PERSON] et al. [33] demonstrated the extraction of relevant features from cross-lingual pre-trained models for downstream tasks like part-of-speech tagging and named entity recognition, providing language-specific knowledge-based information. [PERSON] et al. [34] employed a meta-learning approach to achieve improved cross-lingual transfer with M-BERT, particularly in named entity recognition. However, this method requires re-finetuning the model for each test sample during the inference stage, resulting in substantial computational costs. [PERSON] et al. [35] used language adversarial training to encourage mBERT to generate more language-independent features. [PERSON] et al. [36] considered CLT tasks as domain adaptation problems, and utilized an unsupervised domain adaptation method based on adversarial training to reduce domain differences. [PERSON] et al. [37] achieved significant results in German and Hindi OLD tasks from social media by fine-tuning the mBERT pre-trained model. [PERSON] et al. [38] confirmed that context-based mBERT captures similarities between languages, clustering them by language, and cross-lingual fine-tuning does not disrupt this property. In other words, mBERT encodes part of the language information based on its position in the embedding space, concentrating the encoding of each language to achieve a degree of cross-lingual capability. [PERSON] et al. [39] proposed a method employing SVM and mBERT to construct a cross-lingual model for detecting offensive and misogynistic language on Twitter. [PERSON] et al. [40] enhanced mBERT with transfer learning strategy, transferring knowledge from a resource-rich language to low-resource languages, effectively improving the accuracy of OLD in low-resource languages.