## 3 Proposed Method
### Repeat Question Module
This mechanism calculates the mean and variance of each dimension of the input matrix based on the sample features, stabilizing the data distribution during forward propagation and the gradient during backward propagation. Layer normalization is more suitable for handling long data compared with batch normalization. Normalization focuses on individual sample feature spaces rather than the sentence length and batch size, stabilizing the feature space. The input dimension here is \(M_{embed}\), and the dimension after incrementing is \(cls_{dim}=4\times M_{embed}\). Considering the integrity of the module, we will introduce the computational logic of this module in Section 3.3. A residual additive connection is utilized in the repeat question learning module (Figure 3). This approach concatenates the input of the previous layer onto the output of the current layer, prompting the Transformer encoder to focus on every element of the input information. During the forward propagation, this mechanism strengthens the perception of the classification results, similar to prompting the model to confirm the classification answer under repeated questioning, reducing the oscillation caused by the randomness of the weight matrix. Assuming that \(TransOut_{d}\) represents the output of the current layer, \(Out_{d}\) represents the output of the original Transformer layer, the model retains a copy of  
Figure 3: Residual connection structure of deep RQ-OSPTrans. this output denoted as \(TransOut_{top}\), while \(\lambda\) serves as the guiding factor for the next layer. The formula for the residual connection in the next layer's output is as follows:  
\[Out_{d}=Att_{out}+LayerNorm(MLP(Att_{out})), \tag{9}\]  
\[TransOut_{d+1}=Out_{d+1}+\frac{\lambda}{\lambda+\frac{\sum_{i}\sum_{j}Attention Score_{ij}}{M_{embed}}}TransOut_{drop}. \tag{10}\]  
At the heart of the repetition mechanism lies Equation (10). In a multi-layer Transformer encoder stack, the output of the pre-sequence network layer will affect the learning of the subsequent network, which may lead to a decrease in classification performance since a very small number of classification cues occupy a large attention score and cause the subsequent network to focus only on these parts. Therefore, it is a way to improve the performance by promoting the model to learn more categorical cues and correct the error of the weight allocation of the pre-order network in the subsequent network, and the \"repeated questioning\" mechanism proposed by us is derived from the attention information contained in the attention score matrix. First, we look at all the attention scores of a text as a whole and sum all the semantically allocated attention values. Secondly, to avoid overfitting the model by focusing on only a few key cues in multiple rounds of questioning, we use the word embedding dimension to smooth the attention value. After that, the output amplification weight is calculated with the help of the guide factor \(\lambda\) to scale the results of the previous round of attention learning, and the additive combination with the results of this round of learning is completed to complete a repeated question learning.