# Multi-Task Aspect-Based Sentiment: A Hybrid Sampling and Stance Detection Approach
## 3 Proposed Method
### Self-Train Learning  
The self-training process of MMS will be thoroughly explained in this part. There are five steps in the MMS self-training: setting up the instructor model, creating pseudo labels, setting up the student model, and this model self-training using the meta-weigher. Figure 1: The outline suggested a meta-weigher with a meta-based self-training method (MMS). The display of various data streams uses various color schemes. The first phase, creating artificial instances, oversamples the under-represented class distribution using the SMOTE-OSS-CNN. On the other hand, the learning model's decision-making is directly influenced by the development of synthetic examples closer to the decision boundary. The second phase is setting up the instructor model. The IM is a supervised technique with a labeled dataset \(D_{lab}\) for setting up. To reduce the effect of noise from unlabeled data on MMS, unlabeled data is not used in this training phase. \[H_{s}=BERT_{Taa}(w_{1},\dots,\ w_{L}) \tag{1}\]  
\[\hat{Y}_{s}^{t}=Argmax\ (Softmax(FNN_{Taa}^{t}(H_{S}))) \tag{2}\]  
\((w_{1},\dots,\ w_{L})\) refer to the input of the \(L\) token and \(S\) sentence task \(t\ \in(SE\), \(ASC\), and \(ATE)\). \(H_{s}\) is the hidden representation of \(S\in\ D_{Lab}\). Feed-forward neural network (FNN) with two layers of activation of the \(ReLU\). \(BERT_{Taa}\) is the encoder of the instructor model. The instructor and the student model utilize BERT-large as encoders to allow for a fair comparison with research [40,41]. \(Loss_{Taa}\) is utilized to update the instructor model, which is calculated as the average of the three tasks' cross entropy CE. \[Loss_{Taa}=\frac{1}{\|Tasks\|}\ \sum_{t\ \in Tasks}\sum_{w_{i}\in S}CE\ \left(\hat{Y}_{w_{i} ^{t}}^{t},\ Y_{w_{i}}^{t}\right) \tag{3}\]  
where \(\hat{Y}_{w_{i}}^{t}\in\ \hat{Y}_{s}^{t}\), \(Y_{s}^{t}\) is the gold label sequence, whereas \(Y_{w_{i}}^{t}\in\ Y_{s}^{t}\). \(\hat{Y}_{s}^{t}\) is a predicted label sequence. The third phase is creating pseudo labels. MMS employs the initialized instructor model to produce data pseudo labels of unlabeled \(D_{unl}\), utilizing modeling tools to choose these produced labels and estimate data uncertainty. \(D_{unl}\) is extracted from the initial dataset without label annotation. \(S_{unl}\), specifically, for each sentence without a label, considering dropout, given \(N_{aug}\) forward passes through the instructor model (dropout can introduce stochasticity). With each run using the same model parameters, data augmentation is the term used by MMS to describe the averaged representations of this unlabeled data. Then, for each token within a sub-task \(t\), an averaged representation is translated into a soft pseudo label. \[Logtitts_{s,pseu}^{t}=\frac{1}{N_{aug}}\sum_{i\ \in\ N_{aug}}FNN_{Taa}^{t}\ (H_{s,i}) \tag{4}\]  
\[\hat{Y}_{S,pseu}^{t}=Softmax\ \left(Logtits_{S,\ pseu}^{t}\right), \tag{5}\]  
where \(H_{s,i}\) signifies the BERT hidden statuses of a sentence \(S\) from the \(ith\) forward pass in the instructor model. MMS applies the dropout proposed by [42] to calculate data ambiguity \(unc\) in the following Equation, based on labels of soft pseudo \(\hat{Y}_{S,pseu}^{t}\). \[unc_{pseu}^{t}=\frac{\hat{Y}_{S,pseu}^{t}\times Log\ \hat{Y}_{S,pseu}^{t}}{\sum_{S \in D_{unl}}\hat{Y}_{S,pseu}^{t}\times Log\ \hat{Y}_{S,pseu}^{t}} \tag{6}\]  
The above Equation is based on the idea that if this paper can apply the same model to forecast the same example more than once and the results are various, this paper can compute the entropy from these additional trials to estimate the uncertainty for this example. The last data ambiguity \(unc_{pseu}\), for example, is averaged over various tasks \(t\). The occurrences are complex samples, as evidenced by higher data uncertainty. MMS may not provide helpful information if it provides the self-training student model with too many simple instances. However, a high level of uncertainty can imply that these data examples are noisy.