## 4 Experiments
### Ablation Study
The primary aim of this study is to leverage a multimodal language model to unveil the intricate relationships between characters, therefore addressing the challenge of uneven fusion of multimodal feature information. Notably, upon excluding the pinyin prediction task, there is a discernible decrease in detection/correction F1 scores by 2.6% and 1.9%, respectively. This underscores the effectiveness of the pinyin prediction task in capturing the nuances of Chinese character pronunciation. Similarly, upon removal of the self-distillation module, there is a reduction in detection/correction F1 scores by 2.4% and 2.2%, respectively, affirming the efficacy of the introduced self-distillation module in mitigating the risk of overfitting to pinyin features. To further verify the effectiveness of different encoders, this study conducted ablation experiments with the following configurations on the SIGHAN13 test set to explore encoder selection: (a) SPMSpell (BERT) refers to using BERT as the encoder in the SPMSpell model; (b) SPMSpell (REALISE) refers to using the REALISE model as the encoder in the SPMSpell model; (c) SPMSpell (ChineseBERT) refers to using ChineseBERT as the encoder in the SPMSpell model, which is the proposed SPMSpell model in this paper. The experimental results, as shown in Table 3, display the precision (P), recall (R), and F1 score of sentence-level detection/correction. Overall, compared to using BERT as the encoder, the REALISE and ChineseBERT models, possibly due to incorporating character shape and pinyin information into language model pre-training, achieve better performance. With the assistance of character shape and pinyin information, better performance is attained. When comparing REALISE with the ChineseBERT model, using ChineseBERT as the encoder yields superior results. This may be because the REALISE model is multimodal, emphasizing the integration of text, sound, and visual information, while ChineseBERT focuses on the features of the Chinese language, particularly the fusion of character shape and pinyin information. It can simultaneously capture semantic, character shape, and pronunciation features without encountering the issue of re-fusing different features obtained by different models, effectively addressing the problem of uneven feature information. On the other hand, using ChineseBERT alone as the multimodal encoder backbone further simplifies the overall architecture complexity of the CSC model. \begin{table}
\begin{tabular}{l l c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Detection-Level**} & \multicolumn{3}{c}{**Correction-Level**} & \multicolumn{3}{c}{**Char-Level**} \\  & & **D-P** & **D-R** & **D-F** & **C-P** & **C-R** & **C-F** & **D-F** & **C-F** \\ \hline \multirow{4}{*}{SIGHAN15} & SPMSpell & 81.7 & 85.6 & 83.6 & 79.4 & 83.4 & 81.3 & 88.3 & 92.8 \\ \cline{2-10}  & w/o PP & 79.0 & 82.5 & 81.0 & 77.4 & 80.6 & 79.4 & 87.0 & 90.9 \\ \cline{1-1}  & w/o PP & 80.1 & 82.3 & 81.2 & 78.0 & 80.2 & 79.1 & 87.1 & 91.2 \\ \cline{1-1}  & w/o PP & 80.4 & 84.3 & 82.3 & 77.5 & 80.5 & 79.5 & 87.1 & 90.3 \\ \hline \hline \end{tabular} Note: The dataset used in the ablation experiment was SIGHAN15. In the table results, Detection-level and Correction-level represent sentence-level metrics, Char-level represents character-level metrics, D-P means detection module precision, D-R means detection module recall, D-F means detection module F1 value, C-P, C-R, and C-F are correction module corresponding metrics.  
\end{table} 
Table 2: Results of model ablation experiments. In essence, the performance of SPMSpell experiences a decline when any individual component is removed. This provides compelling evidence for the effectiveness of each component within our approach, emphasizing the integral role played by each element in contributing to the overall efficacy of SPMSpell.