# A Chinese Few-Shot Text Classification Method Utilizing Improved Prompt Learning and Unlabeled Data
## 5 Discussion
### Ablation Experiment  
CIPLUD consists of two modules: the Multiple Masks Optimization-based Prompt Learning (MMOPL) module and the One-Class Support Vector Machine-based Unlabeled Data Leveraging (OCSVM-UDL) module. We conducted a detailed analysis of the effect of the different varying components of the CIPLUD model. We further report the ablation experiments on the four FSTC tasks (EPRSTMT, CSLDCP, TNEWS, and IFLYTEK), as shown in Table 5.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **EPRSTMT** & **CSLDCP** & **TNEWS** & **IFLYTEK** & **Avg** \\  & **(Acc. \%)** & **(Acc. \%)** & **(Acc. \%)** & **(Acc. \%)** & **(Acc. \%)** \\ \hline Human & 90.0 & 68.0 & 71.0 & 66.0 & 73.6 \\ Fine-Tuning & 66.5 & 57.0 & 51.6 & 42.1 & 54.3 \\ PET & 84.0 & 59.9 & 56.4 & 50.3 & 62.7 \\ P-tuning & 80.6 & 56.6 & 55.9 & 52.6 & 61.4 \\ EFL & 76.7 & 47.9 & 56.3 & 52.1 & 58.3 \\ MMOPL & 82.1 & 59.8 & 56.4 & 52.2 & 62.6 \\ CIPLUD & **85.4** & **60.4** & **57.2** & **52.8** & **64.0** \\ \hline \hline \end{tabular}
\end{table}
Table 4: The comparison results between our proposed model, CIPLUD, and the baselines. the Human row refers to the performance of human annotators on the tasks, and the underlined values indicate the second-best results achieved among the baseline methods. We bold-marked the highest score in each column.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **EPRSTMT** & **CSLDCP** & **TNEWS** & **IFLYTEK** \\  & **(Acc. \%)** & **(Acc. \%)** & **(Acc. \%)** & **(Acc. \%)** \\ \hline w/o MMOPL & 77.8 & 56.7 & 52.0 & 50.6 \\ w/o OCSVM-UDL & 82.1 & 59.8 & 56.4 & 52.2 \\ \hline CIPLUD & 85.4 & 60.4 & 57.2 & 52.8 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The comparison results between our proposed method with or without MMOPL and OCSVM-UDL.  
For \"w/o MMOPL\", we directly removed the multiple masks of the prompt template and replaced two masks; a single mask is meaningless for Chinese labels; for \"w/o OCSVM-UDL\", we removed the iterative training process using unlabeled data. Our results indicate that our approach degrades in performance without every module in most settings. This means that when any of the modules are removed, our approach does not perform as well as when all the modules are present. This indicates that individual modules are highly interdependent, and the performance of our approach relies on the presence of all the modules. Then, we found that deleting the MMOPL greatly decreased the classifier performance, and deleting the OCSVM-UDL slightly reduced the classifier performance. This is likely since MMOPL can capture more complex relationships between the prompt information and label token, while OCSVM-UDL is limited to increasing the scale of trainable data. By deleting the MMOPL, the classifier is not able to take advantage of its more complex algorithm, resulting in a decrease in performance. On the other hand, the OCSVM-UDL algorithm is limited to increasing the scale of trainable data, so deleting it will only result in a slight decrease in performance. In addition, we found that OCSVM-UDL classifiers have poor performance growth on data sets with more tag types (CSLDCP and IFLYTEK). This may be the result of the OCSVM-UDL module having difficulty obtaining uniform sampling when there are more than 50 label categories. This causes the number of recalled pseudo-labels to be imbalanced, resulting in a decrease in the accuracy of the prediction results. Overall, the results of this experiment show the importance of the MMOPL and OCSVM-UDL modules in our approach.