# Chinese Named Entity Recognition Model Based on Multi-Task Learning
## 4 Experiment and Analysis
### Dataset
#### 4.3.5 Training Efficiency Performance of the MTL-BERT Model  
Finally, we conducted experiments to compare the training efficiency of the MTL-BERT and BERT models on two datasets. We randomly selected three training rounds as samples and trained the models under the same hardware environment and parameter settings. The results, shown in Table 7, indicate that the MTL-BERT model has a significantly shorter convergence time on both datasets, with a 1/3 reduction in training time on the larger MSRA dataset. This demonstrates the superiority of multi-task learning in terms of learning efficiency.  
There are two main reasons for this. Firstly, as discussed in the introduction, the label categories of the MTL-BERT model are greatly reduced during multi-task learning. Secondly, the feature interactions between subtasks allow the boundary-labeled non-entity information to propagate to the type-labeled task, preventing ineffective learning on non-entity words. These two key factors help the network save computational costs during  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{2}{c}{**CoNLL-2003**} & \multicolumn{4}{c}{**MIT-Movie**} \\ \cline{2-7}  & **P** & **R** & **F1** & **P** & **R** & **F1** \\ \hline BERT & 90.71 & 92.10 & 91.42 & 67.5 & 72.5 & 69.9 \\ W2 NER & 92.71 & 93.44 & 93.07 & 69.1 & 74.2 & 71.2 \\ MTL-BERT & 91.91 & 93.73 & 92.82 & 68.1 & 73.8 & 70.8 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of the performance of the BERT and MTL-BERT models regarding the separate F1 values of the two subtasks (%).  
\begin{table}
\begin{tabular}{l l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Weibo NER**} & \multicolumn{3}{c}{**MSRA**} & \multicolumn{3}{c}{**OntoNote4.0**} \\ \cline{3-11}  & & **P** & **R** & **F1** & **P** & **R** & **F1** & **P** & **R** & **F1** \\ \hline [PERSON] et al., 2015 & LR-CNN & 57.1 & 66.7 & 58.9 & 94.5 & 92.9 & 93.7 & 80.12 & 84.32 & 82.11 \\ [PERSON] and [PERSON], 2016 & BiLSTM-CRF & 60.8 & 52.9 & 56.6 & 92.3 & 92.4 & 92.4 & 79.27 & 76.34 & 78.22 \\ [PERSON] et al., 2018 & Lattice-LSTM & 53.0 & 62.3 & 59.8 & 93.6 & 92.8 & 93.2 & 80.56 & 83.22 & 81.93 \\ [PERSON] et al., 2018 & BERT & 66.4 & 67.2 & 66.8 & 94.0 & 94.1 & 94.0 & 80.94 & 85.95 & 83.28 \\ [PERSON] et al., 2020 & BERT-WWM & - & - & 68.5 & - & - & 95.3 & 81.21 & 84.39 & 83.23 \\ [PERSON] et al., 2021 & ChineseBERT & 68.27 & 69.78 & 69.02 & 94.6 & 95.4 & 94.6 & 80.77 & 83.65 & 82.18 \\ [PERSON] et al., 1, 2022 & W2 NER & 70.84 & 70.84 & 70.84 & 96.08 & 96.10 & 96.12 & 82.31 & 83.36 & 83.08 \\ ours & MTL-BERT & 74.9 & 72.7 & 73.8 & 96.5 & 96.6 & 96.5 & 85.9 & 87.7 & 86.7 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparative experimental results of Chinese named entity recognition task (%).  
\begin{table}
\begin{tabular}{l l c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**AG001**} & \multicolumn{3}{c}{**CCKS2019**} \\ \cline{2-9}  & & **Epoch** & **P** & **R** & **F1** & **Epoch** & **P** & **R** & **F1** \\ \hline [PERSON] et al., 2018 & BERT & 10 & 89.64 & 80.23 & 84.5 & 10 & 78.34 & 83.22 & 81.56 \\ ours & MTL-BERT & 3 & 92.19 & 88.90 & 90.79 & 3 & 89.33 & 88.92 & 89.03 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Domain-specific Named Entity Recognition Comparison Experiment Results (%).  
decoding and back-propagation, thus effectively improving the learning efficiency of the model.