## 2 Related Works
However, these methods overlook the differences between the source and target languages during the transfer learning process, impacting detection performance in the target domain. This oversight leads to negative transfer effects, which compromise detection performance in the target domain. Negative transfer refers to the phenomenon in transfer learning where the transfer of knowledge or models from a source domain to a target domain not only fails to improve performance but may actually degrade it [41]. Significant linguistic differences exist between source and target languages, encompassing grammar, vocabulary usage habits, and more. These differences can hinder effective model transfer in zero-shot scenarios [42]. Variations in language usage backgrounds and cultural contexts across different languages can affect the model's understanding of text meaning, thereby impacting the effectiveness of transfer learning [43]. Disparities in data distribution between source and target languages, including differences in data volume, types, and quality, can also lead to poor model performance in the target language [44]. To mitigate the risks of negative transfer, this paper leverages the multilingual pre-training capability of mBERT. This enables the model to learn universal language representations. By minimizing the Sinkhorn distance, a stable feature alignment mechanism is established between source and target languages. This helps ensure the model effectively maintains consistency in the feature space during transfer, reducing negative transfer due to language differences. Furthermore, handling and masking pivot words during training enhances the model's robustness to the target language. Additionally, a joint domain-shared feature training strategy allows the model to simultaneously consider and utilize information from both source and target languages. This effectively addresses potential negative transfer issues in zero-shot CLT learning, enhancing the model's generalization capability and stability. In the realm of cross-lingual OLD, ML models such as LR and SVM offer high interpretability, crucial for understanding how models make predictions and which features influence these predictions most significantly [45]. LR classifies by learning the weights of features, directly interpreting each feature's weight as its contribution to the final classification. This transparency makes LR intuitive and straightforward in terms of interpretability, as its linear decision boundary visibly shows which combinations of feature values lead to specific classification decisions. SVM's decisions hinge on support vectors--samples on the decision boundary--determining not only its location but also aiding in understanding the model's certainty and accuracy in delineating between different classes. When using kernel functions like the Radial Basis Function (RBF) kernel, SVM maps data into high-dimensional space to find the optimal decision hyperplane, where kernel choice affects model interpretability. In contrast, DL models are often dubbed \"black box\" models due to their complex internal structures and parameters, complicating direct explanations of decision processes and feature selection principles [46]. The interpretability of DL models tends to be more indirect, often relying on a deeper understanding of their internal mechanisms. For instance, models like mBERT, a pretrained language model, operate with intricate internal mechanisms learned via self-attention on vast text datasets. Consequently, the meanings of its features may not be as intuitively clear as those derived from traditional linear models like LR and SVM. Moreover, transfer learning emphasizes sharing knowledge across different tasks or domains, where interpretability hinges on effectively selecting and transferring knowledge from the source domain and adapting it to the target domain. Feature alignment and transfer rules play pivotal roles in this process.