## 4 Diverse Model Stacking Ensemble  
Stacking, also known as stacked generalization, is an ensemble learning technique that involves creating multiple base models, often using different learning algorithms, and then combining their predictions using a higher-level model called the meta-learner or stacker [64, 65, 66]. Each base classifier is trained on the same training dataset. The meta-learner is trained on a separate dataset created using K-fold cross-validation; that is, for each fold, the base models are trained on a subset of the dataset and then utilized to make predictions on the remaining dataset. Finally, the base models' predictions for each fold are combined into a new dataset, also know as meta-features, to be used for training the meta-learner [67]. The goal of the stacking ensemble model is to exploit the diversity and complementary strengths of the base classifiers to improve overall predictive performance [68].  
We created six base models in our proposed method, as shown in Figure 2. The models were created based on machine learning, transfer learning via BERT, few-shot methods, and zero-shot classification. The diverse set of learning paradigms may help create models that are able to capture different patterns from the data and consequently have different strengths and characteristics. The traditional machine learning algorithms can perform reasonably well with a smaller amount of labeled data compared to complex deep learning models, such as Transformer-based models [69]. On the other hand, transfer learning models, such as BERT-based models, capture deep semantic relationships, making them highly effective at understanding complex context and nuances in text. In addition, unlike traditional machine learning, fine-tuned BERT models can generalize well to new or unseen data even if the distribution shifts significantly [70].  
Few-shot methods, including techniques such as nearest neighbor embedding lookup and data augmentation, are powerful solutions for mitigating the challenges posed by small amounts of labeled data. Data augmentation increases data diversity by creating new training examples through the application of various transformations on the existing labeled data. This process enables the model to generalize better to unseen textual examples with  
Figure 2: The architecture of the proposed method.  
new vocabularies. At the same time, augmentation reduces the risk of overfitting on the limited labeled data by introducing variability into the training data [71; 72]. On the other hand, nearest neighbor embedding lookup leverages the idea that similar examples in the feature space should have similar labels. Leveraging the inherent structure of existing data can lead to improved generalization and performance [73]. Zero-shot learning allows the model to extend its capabilities to new classes without additional labeled data or retraining. It encourages the model to learn high-level semantic relationships between classes and to recognize patterns and attributes that are common across different classes [74].  
To build a meta-learner for combining the predictions of the base classifiers, we employed SVM. The following sections illustrate the details of each base classifier utilized in our proposed method.