# Uncertainty-Aware Active Meta-Learning for Few-Shot Text Classification
## 5 Experiments
### Few-Shot Text Classification  
The Amazon Review Sentiment Classification (ARSC) dataset was used to evaluate the performance of the method proposed in the field of low-resource NLU, which considers the domain as a task type in the setting of meta-learning. ARSC has text reviews of 13 domains for sentiment analysis. For each data, three tasks are composed based on the star score, and there are a total of 69 tasks. Among them, four domains--Books, DVDs, Electronics, and Kitchen--are considered test domains; meta training is performed in other domains, and few-shot text classification is performed based on how well learning is performed in the test domain. In other words, performing ARSC positive and negative binary classification is the same, but the ability to quickly adapt to domains with different domains is important. Of the total 23 domains of ARSC, 19 domains are used for meta-training tasks, and the other four domains are used for meta-testing tasks.  
The training batch consists of 5 positive and 5 negative support samples, along with 27 positive and 27 negative query samples, totaling 64 instances per batch. The task is a binary classification that distinguishes between positive and negative, and it is a 2-way 5-shot problem because it utilizes five support data samples for each class. In the development and test phases, 54 queries are sampled, along with five support data samples for each class, and a total of 183 batches are utilized. Induction networks were used to train the model, and the model was generalized by performing 10,000 iterations for meta-training.  
Table 3 compares and evaluates the average accuracy of episodes for a total of 10,000 test tasks. As a result of the experiment, compared to the induction networks implemented directly in this research, the proposed UA-AML induction networks model with USB loss showed that the performance could be improved more. As the existing induction networks show excellent performance, a model that replaces the encoder part among the three modules of the existing networks with a BERT model can be considered.  
Next, in the learning process of the proposed UA-AML method, one can adjust the ratio of whether uncertainty is applied to the loss function or confidence in adjusting the gradients of the data using uncertainty. This ratio adjustment is made by replacing the uncertainty coefficient with confidence in the existing USB loss. Therefore, in this experiment, performance evaluation was performed by sequentially increasing the ratio of samples to which uncertainty is applied to 20%, 50%, 80%, and 100%. As shown in Figure 3, when the ratio selected as the hard sample was 20%, it showed the lowest performance. In contrast, when the ratio selected as the hard sample was 80%, it recorded 85.71% accuracy and showed the best performance. As a result of this experiment, it can be confirmed that the hard sample with high uncertainty has a positive effect on model learning, but the most positive learning effect appears only when the easy sample in an appropriate ratio is mixed. In addition, since these ratios may vary depending on the characteristics of each data or model, research on a methodology that can adaptively adjust the ratio is needed.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Text Encoder** & **5-Way 1-Shot Acc.** & **10-Way 1-Shot Acc.** \\ \hline BERT-PAIR [39] & 74.56\% & 73.09\% \\ UA-AML & 75.03\% & 73.78\% \\ BERT-PAIR & \multicolumn{1}{c}{} & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Few-shot relation classification accuracy for FewRel development dataset in None-of-the-Above Detection (NOTA) problem.