# Grammar Correction for Multiple Errors in Chinese Based on Prompt Templates
## 4 Experiments
### Results and Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **FN** & **Precision** & **Recall** & **F0.5** \\ \hline Model 1 & 3401 & 55.51 & 7.91 & 25.19 \\ Model 2 & 3511 & 51.13 & 4.33 & 16.18 \\ Model 3 & 3192 & 23.94 & 18.45 & 22.59 \\ Model 4 & 3508 & 23.45 & 3.33 & 10.63 \\ Model 5 & 3598 & 7.23 & 2.76 & 5.46 \\ Our Model & 3097 & 38.39 & 20.08 & 32.47 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of Error-Correction Performance. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F0.5** \\ \hline Model 1 & 69.62 & 9.99 & 31.73 \\ Model 2 & 67.73 & 5.75 & 21.47 \\ Model 3 & 33.11 & 25.46 & 31.23 \\ Model 4 & 35.84 & 5.09 & 16.23 \\ Model 5 & 12.82 & 4.81 & 9.62 \\ Our Model & 70.66 & 28.58 & 54.58 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of Error-Detection Performance. The following observations and conclusions were drawn by comparing the model in this paper and existing methods on four datasets with different error numbers:  
1. The proposed model achieved the best performance in terms of F0.5 on all datasets, and its precision consistently remained relatively high compared to other models. However, some models had higher precision on specific datasets. However, the proposed model demonstrated an advantage in F0.5 scores when considering a recall. This indicates that the proposed model balanced identifying all errors (recall) and avoiding false positives (precision). 2. All models performed relatively well due to the higher occurrence of word-related errors in the Mu_3 dataset and word-related errors being relatively more straightforward to detect and correct than disorder-related or omission-related errors. Additionally, on the Mu_1, Mu_2, and Mu_4 datasets, as the number of errors increased, the performance of all models declined, mainly reflected in a decrease in recall. However, the proposed model exhibited a relatively minor decrease in recall, suggesting that it maintained relatively stable performance when handling more complex sentences with more errors. 3. Furthermore, the GECToR model, based on sequence labeling, showed good recall performance on the dataset, but lower precision. This is because it requires multiple editing operations to correct all errors in sentences with multiple errors. However, complex combinations of errors impose higher demands on the model regarding selecting editing operations and their order, leading to decreased precision. On the other hand, the proposed model achieved a better balance between the two, resulting in the highest F0.5 score. 4. The comparative experimental results show that our model demonstrates significant advantages in handling sentences with multiple errors, especially regarding the F0.5  
Figure 5: Comparison of performance of different models with different number of errors. score. Notably, for other models, as the number of errors in a sentence increases, the performance of the models tends to decrease. However, by employing a unique method of Chinese grammar error-correction prompt templates, our model can maintain relatively stable performance, even when processing sentences with high error density. The discussion in this section further confirms the superiority of our model over existing models in handling sentences with multiple errors, especially in terms of an F0.5 score. It also verifies the effectiveness of the Chinese grammar-correction prompt template method we proposed, which can help the model better understand and deal with error information, thus improving the accuracy of error correction and the stability of the model. When correcting errors, traditional models may be influenced by erroneous character information, but our model can avoid this situation. Specifically, our model dynamically updates the error-correction information into the prompt template based on the error type, thus fully utilizing BERT's masked prediction capability for more accurate prediction during the error-correction process.