## 4 Experiments and Results
### Experimental Results
The purpose of these groups was to isolate and test the individual contributions of key model components or strategies, namely as follows:  
Experimental group 1: RoBERTa + multi-head attention, in which a multi-head attention mechanism is added to improve the model's ability to model different positions and semantic relationships. Experimental group 2: RoBERTa + multi-head attention + residual connection. Based on experimental group 1, residual connections are added to promote the flow of gradients and improve the training stability. The rationality of the model is further verified. Experimental group 3: RoBERTa + multi-head attention + the inconsistency loss function. Based on experimental group 1, the inconsistency loss function is introduced to increase the model's diversity in modeling the input data. The rationale behind these choices was to understand the impact of each key component on the model performance and identify the most crucial factors for the task. These results allowed us to pinpoint which aspects contributed most significantly to the model's success. The experimental results are shown in the following, Table 8.  
In terms of training speed, our dataset's final model showed some promising results. Compared to experimental group 1, our model's training speed improved by approximately 10%. Compared to experimental group 2, our model's training speed remained steady. Despite the lack of a significant improvement, our model still maintained a comparable training efficiency to that in experimental group 2. In comparison with experimental group 3, our model demonstrated an approximate 4% increase in training speed. This indicates that our model can utilize computational resources more efficiently, accelerating its conver  
Figure 5: Experimental results for different loss functions in our dataset using the RoBERTa-MHARC model. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{3}{c}{**FBS**} & \multicolumn{3}{c}{**Our Dataset**} \\ \cline{2-7}  & **Precision** & **Recall** & **F1** & **Precision** & **Recall** & **F1** \\ \hline RoBERTa + multi-head attention & 97.20 & 96.94 & 97.02 & 97.32 & 97.38 & 97.31 \\ \hline RoBERTa + multi-head attention + residual & 97.27 & 96.97 & 97.09 & 97.92 & 97.97 & 97.93 \\ \hline RoBERTa + multi-head attention + inconsistency loss & 94.57 & 95.26 & 94.29 & 97.53 & 97.44 & 97.42 \\ \hline RoBERTa-MHARC (ours) & **97.90** & **97.47** & **97.65** & **98.11** & **98.12** & **98.10** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Table of ablation experiment results. gence speed and completing more training steps within the same timeframe. Additionally, on the FBS dataset, our model also exhibited satisfactory training speed advantages. Compared to experimental group 1, our model's training speed increased by 3.6% and by 2% compared to experimental group 2. Although it remained on par with experimental group 3, we still maintained a certain level of training efficiency. It is worth noting that despite increasing the model's complexity, our training time did not increase but rather decreased to some extent. This was due to our careful design and adjustment of the model to ensure that the performance improvements did not significantly increase the training time. This further validates the efficiency. The ablation experiment results show that the introduction of residual connections and the inconsistency loss function plays a certain positive role in improving the model's performance. Residual connections improve the training stability by promoting the flow of gradients; the inconsistent loss function increases the model's diversity in modeling input data.