# Prompt-Based Graph Convolution Adversarial Meta-Learning for Few-Shot Text Classification
## 4 Experiments
### Results and Analysis
#### 4.4.2 Ablation Experiment  
We conducted an ablation study on the Amazon dataset and 20 News dataset to validate the effectiveness of each component of our PGCA. For the ablation tests, two 5-way 1-shot tasks were set up, and two tasks were internally looped for a single external loop update. Table 3 displays the outcomes of the ablation trial.  
We first removed the domain discriminator and source set to verify the contribution of adversarial network to the classification task. The performance in the 5-way 1-shot tasks decreased by 0.22% and 0.35%, respectively. This confirms that our adversarial network helps to improve model performance.  
We then investigated the advanced performance of GCN in extracting features. Referring to the work of [17], we replaced the GCN in the meta-knowledge extractor with a bi-directional LSTM, and the performance in the 5-way 1-shot tasks decreased by 0.95% and 0.41%, respectively. Thus, it is more effective for us to use GCN as a meta-knowledge extractor to extract the meta-knowledge contained in sentences.  
Finally, we removed the meta-knowledge extractor and considered only the cue-based pre-trained language model as an encoder, with a performance drop of 1.13% and 0.59% in the 5-way 1-shot tasks. We argue that considering only the cue-based and trained language model as an encoder loses some of the information of the sentence itself, leading to a performance drop in the classification task. At the same time, our meta-knowledge  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Amazon**} & \multicolumn{2}{c}{**HuffPost**} & \multicolumn{2}{c}{**Reuters**} & \multicolumn{2}{c}{**20 News**} \\ \cline{2-9}  & **1-shot** & **5-shot** & **1-shot** & **5-shot** & **1-shot** & **5-shot** & **1-shot** & **5-shot** \\ \hline
**MAML** & 39.3 & 47.2 & 43.7 & 54.3 & 84.8 & 94.0 & 33.8 & 43.7 \\
**PROTO-BERT** & 68.1 & 82.5 & 52.9 & 69.3 & 86.7 & 93.9 & 37.8 & 45.3 \\
**MLADA** & 68.4 & 86.0 & 45.0 & 64.9 & 82.3 & 96.7 & 59.6 & 77.8 \\
**SaAML** & 71.47 & 86.37 & 51.26 & 69.44 & - & - & 70.79 & 84.30 \\
**FROG-GNN** & 71.5 & 83.6 & 54.1 & 69.6 & - & - & - & - \\
**PBML** & 80.03 & 87.58 & 71.54 & 75.71 & 94.91 & 96.97 & 87.50 & 92.32 \\
**PGCA (ours)** & **80.36** & **87.68** & **72.10** & **76.10** & **95.20** & **97.29** & **88.09** & **92.45** \\ \hline \hline \end{tabular}  
* The bolded numerals in the table indicate optimal performance.  
\end{table}
Table 2: The reported findings demonstrate the performance of 5-way 1-shot and 5-way 5-shot text classification on four benchmark datasets in terms of accuracy (%).  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Amazon** & **20 News** \\ \hline PGCA & 79.55 & 88.09 \\ \(-\)w/o DD & 79.32 & 87.74 \\ \(-\)w/o GCN & 78.60 & 87.68 \\ \(-\)w/o DD + GCN & 78.42 & 87.50 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the accuracy (%) of ablation experiments using 5-way, 1-shot settings on the Amazon dataset and 20 News dataset. DD stands for domain discriminator.  
extractor can obtain more feature information through sentence interactions, which helps to improve classification performance.