# Visual and Phonological Feature Enhanced Siamese BERT for Chinese Spelling Error Correction
## 3 Approach
### Main Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Character-Level**} & \multicolumn{6}{c}{**Sentence-Level**} \\ \hline  & \multicolumn{3}{c}{**Detection-Level**} & \multicolumn{3}{c}{**Correction-Level**} & \multicolumn{3}{c}{**Detection-Level**} & \multicolumn{3}{c}{**Correction-Level**} \\ \hline SIGHAN 2013 & D-P & D-R & D-F & C-P & C-R & C-F & D-P & D-R & D-F & C-P & C-R & C-F \\ \hline LMC [28] & 79.8 & 50.0 & 61.5 & 77.6 & 22.7 & 35.1 & (-) & (-) & (-) & (-) & (-) \\ SL [3] & 54.0 & 69.3 & 60.7 & (-) & (-) & 52.1 & (-) & (-) & (-) & (-) & (-) \\ PN [7] & 56.8 & 91.4 & 70.1 & 79.7 & 59.4 & 68.1 & (-) & (-) & (-) & (-) & (-) \\ FASpell [11] & (-) & (-) & (-) & (-) & (-) & (-) & 76.2 & 63.2 & 69.1 & 73.1 & 60.5 & 66.2 \\ \hline BERT-Embed [8] & 80.6 & 88.4 & 84.3 & 98.1 & 87.2 & 92.3 & 79.0 & 72.8 & 75.8 & 77.7 & 71.6 & 74.6 \\ BERT-Linear & 83.7 & 87.9 & 85.7 & **98.6** & 86.7 & 92.3 & 83.4 & 76.5 & 79.8 & **82.0** & 75.2 & 78.4 \\ Soft-Masked BERT [12] \textbackslash{} \ddagger\textbackslash{} & **84.9** & 87.6 & 86.2 & 97.5 & 85.4 & 91.1 & **84.0** & 77.2 & **80.4** & 81.5 & 74.8 & 78.0 \\ SpellGCN [8] & 82.6 & 88.9 & 85.7 & 98.4 & **88.4** & **93.1** & 80.1 & 74.4 & 77.2 & 78.3 & 72.7 & 75.4 \\ FES-BERT & 84.4 & **89.6** & **86.9** & 98.4 & 88.2 & 93.0 & 82.9 & **77.7** & 80.2 & 81.3 & **76.2** & **78.6** \\ \hline SIGHAN 2014 & D-P & D-R & D-F & C-P & C-R & C-F & D-P & D-R & D-F & C-P & C-R & C-F \\ \hline LMC [28] & 56.4 & 34.8 & 43.0 & 71.1 & 50.2 & 58.8 & (-) & (-) & (-) & (-) & (-) \\ SL [3] & 51.9 & 66.2 & 58.2 & (-) & (-) & 56.1 & (-) & (-) & (-) & (-) & (-) \\ PN [7] & 63.2 & 82.5 & 71.6 & 79.3 & 68.9 & 73.7 & (-) & (-) & (-) & (-) & (-) \\ FASpell [11] & (-) & (-) & (-) & (-) & (-) & (-) & 61.0 & 53.5 & 57.0 & 59.4 & 52.0 & 55.4 \\ \hline BERT-Embed [8] & 82.9 & 77.6 & 80.2 & 96.8 & 75.2 & 84.6 & 65.6 & 68.1 & 66.8 & 63.1 & 65.5 & 64.3 \\ BERT-Linear & 82.3 & 78.1 & 80.1 & 96.6 & 75.4 & 84.7 & 66.4 & 67.8 & 67.1 & 64.0 & 65.3 & 64.7 \\ Soft-Masked BERT [12] \textbackslash{} \ddagger\textbackslash{} & **84.1** & 77.5 & 80.6 & 95.4 & 73.9 & 83.3 & 66.9 & 69.7 & 68.3 & 63.8 & 66.5 & 65.1 \\ SpellGCN [8] & 83.6 & 78.6 & **81.0** & **97.2** & 76.4 & 85.5 & 65.1 & 69.5 & 67.2 & 63.1 & 67.2 & 65.3 \\ FES-BERT & 83.9 & **78.7** & **81.3** & 97.1 & **76.5** & **85.6** & **67.6** & **70.1** & **68.8** & **65.6** & **68.0** & **66.8** \\ \hline SIGHAN 2015 & D-P & D-R & D-F & C-P & C-R & C-F & D-P & D-R & D-F & C-P & C-R & C-F \\ \hline LMC [28] & 83.8 & 26.2 & 40.0 & 71.1 & 50.2 & 58.8 & (-) & (-) & (-) & (-) & (-) \\ SL [3] & 56.6 & 69.4 & 62.3 & (-) & (-) & 57.1 & (-) & (-) & (-) & (-) & (-) \\ PN [7] & 66.8 & 73.1 & 69.8 & 71.5 & 59.5 & 69.9 & (-) & (-) & (-) & (-) & (-) \\ FASpell [11] & (-) & (-) & (-) & (-) & (-) & (-) & (-) & 67.6 & 60.0 & 63.5 & 66.6 & 59.1 & 62.6 \\ \hline BERT-Embed [8] & 87.5 & 85.7 & 86.6 & 95.2 & 81.5 & 87.8 & 73.7 & 78.2 & 75.9 & 70.9 & 75.2 & 73.0 \\ BERT-Linear & 86.7 & 83.6 & 85.1 & 94.3 & 78.9 & 85.9 & 76.6 & 78.0 & 77.3 & 73.6 & 74.9 & 74.2 \\ Soft-Masked BERT [12] \textbackslash{} \ddagger\textbackslash{} & 88.0 & 84.5 & 86.2 & 94.9 & 80.1 & 86.9 & 77.5 & 79.1 & 78.3 & 74.5 & 76.0 & 75.2 \\ SpellGCN [8] & 88.9 & **87.7** & **88.3** & 95.7 & 83.9 & 89.4 & 74.8 & 80.7 & 77.7 & 72.1 & 77.7 & 75.9 \\ FES-BERT & **89.4** & 87.3 & **88.3** & **97.1** & **84.8** & **90.5** & **79.6** & **82.2** & **80.9** & **78.0** & **80.5** & **79.2** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The performance of our method and baseline models (%). D, C denote the detection, correction, respectively. P, R, F denote the precision, recall and F1 score, respectively. Best results are in **bold**. Following experiment setting of [8], We performed additional fine-tuning on SIGHAN13 for 3 epochs as the data distribution in SIGHAN13 differs from other datasets, e.g., \(\textbackslash{}^{\ddagger\textbackslash{}}\textbackslash{}^{\ddagger\textbackslash{}}\) are rarely distinguished. Dataset.