# A Chinese Grammatical Error Correction Method Based on Iterative Training and Sequence Tagging
## 4 Experiments
### Overall Results  
To evaluate the effectiveness of our model for Chinese GEC, we conducted experiments on the NLPCC 2018 dataset. Table 2 shows the performance of our approach compared with recent models for Chinese GEC. The first column shows some Chinese GEC methods, and the second column shows the time required to decode the 2000 sentences of the NLPCC 2018 test dataset. The GEC results are presented in the last three columns, i.e., precision, recall and F0.5. The models in the top group are all classical single models for the Chinese GEC task. The second group shows the three best-performing teams in the NLPCC 2018 Chinese grammar error correction competition. The best results were all obtained using the joint model. The bottom group includes the baseline model and the approach proposed in this paper. In order to verify the effectiveness of the several improvement methods proposed in this paper, we used the baseline model for experiments using each of the three improvement methods, and the experimental results are also shown in the bottom group. The GECToR model achieved a 29.8% F0.5 measure. There is a gap between this baseline model and the leading Chinese GEC models such as the YouDao model [29], the AliGM model [30] and the BLCU model [13]. However, our model achieved an F0.5 score of 31.8%, which was higher than the best-performing BLCU model [13]. It is worth noting that the top three models in NLPCC 2018 Task 2 were ensemble models, but our single model still outperformed them. Our model does not conflict with the ensemble method, and it is theoretically possible to further improve the performance using this method. Moreover, our proposed approach is comparable to the baseline in terms of decoding speed and far better than the autoregressive models such as Seq2 Seq, BERT-encode and BERT-fused. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Split** & **Sentence** & **Token src** & **Token tgt** & **Error Token** & **Redundant Errors** & **Missing Errors** & **Ordering Errors** & **Selection Errors** \\ \hline Train & 1.3 M & 23.7 M & 25.0 M & - & - & - & - & - \\ Dev & 5000 & 99.7 K & 103.2 K & - & - & - & - & - \\ Test & 2000 & 58.9 K & - & 4371 & 911 & 1147 & 176 & 2137 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Details of NLPCC 2018 test setWe conducted separate experiments on each of the three proposed improvements based on GECToR, and the experimental results showed that all three improvements improved the model performance. The CRF layer increased the focus of the GECToR model on the overall annotation results, which improved the F0.5 measure by 0.2%. The use of focus loss mitigated label classification imbalances, increasing false positives and resulting in a 0.9% reduction in the recall, while significantly increasing the precision by 1.5%. The iterative training method solved the exposure bias problem that exists in GECToR and led to the most obvious improvement in performance, with a 1.6% improvement in the F0.5 measure. We concluded that the GECToR method does have an exposure bias problem, and that solving the problem by iterative training can further improve the performance of the model. Finally, the combination of the above three improvements resulted in a 2% improvement in the F0.5 measure, which verifies the effectiveness of our proposed methods. The value of the focal loss hyperparameter \(\gamma\) can affect the performance of the model. In this study, several different hyperparameters with different focal losses were tried, and the experimental results are shown in Figure 6. Finally, we chose 0.5 as the value of the focal loss hyperparameter \(\gamma\) for CGEC-IT. We also investigated the effect of different numbers of iterations on the performances of the baseline method and the method described in this paper, and the experimental results are shown in Figure 7. It can be seen that the highest value of F0.5 was obtained when the maximum number of iterations was three. Continuing to increase the number of iterations