# Prompt Language Learner with Trigger Generation for Dialogue Relation Extraction
## 4 Experiments
### Experimental Results
#### 4.2.2 Few-Shot Setting  
Table 3 shows the few-shot results according to the number of shots \(K\). In the development set, the model where MLM and manual initialization were used (\(MLM+minit\)) obtained the overall highest scores both at F1 and F1\(c\) scores regardless of \(K\), demonstrating significant performance improvements compared to the baseline model. For example, when \(K\) is 8, the \(MLM+minit\) model outperformed the BERT\({}_{s}\) model by 12.04%p and 10.93%p at F1 and F1\(c\) scores, respectively. In the test set, except for the F1 score of \(K=8\), the \(MLM+minit\) also showed the overall highest performances at F1 and F1\(c\) scores, achieving 10.23%p, 4.27%p, 4.51%p of performance improvements at F1 and 8.88%p, 3.7%p and 2.43%p at F1\(c\), when \(K\) is 8, 16 and 32, respectively, compared to the baseline BERT\({}_{s}\). When \(K\) is 8, the model that demonstrated the highest test F1 score is the model with the generated triggers, MLM method and manual initialization method (\(GenTr+MLM+minit\)), showing 37.95. In particular, when the generated triggers were supplemented to the baseline model (\(GenTr\)), the model demonstrated the most significant performance increase when \(K\) is 8, showing a performance gain of 8 points: 6.15%p and 5.92%p at F1 and F1\(c\) scores in the development set and 4.04%p and 3.57%p in the test set. In the case of \(K=32\) of the test set, the F1 and F1\(c\) scores of the \(GenTr\) model marginally dropped by 0.84%p and 1.56%p, respectively. Additionally, unlike with the full-shot setting, among the \(GenTr\), \(MLM\) and  
Figure 2: This figure illustrates the difference between F1 and F1\(c\) performance of each model. The figure on the left is the result from the validation set, and the figure on the right is the result from the test set. The dotted lines indicate BERT\({}_{s}\), the baseline model, and each dot (*) represents the results of models to which each methodology was applied. Red dots represent the results of the fine-tuned model, and blue dots represent those of the prompt-based model. \(GenTr+MLM\) models, the model with both the MLM method and the generated triggers (\(GenTr+MLM\)) did not guarantee the best overall performance in the development set, and the \(MLM\) model did not achieve the most significant overall performance increase in the test set. According to the change in \(K\), the model with the most performance among the three has changed. Figure 3 demonstrates the performance change according to the K value of each model in the few-shot settings of the test set. The chart on the left shows the change in the F1 score according to the K value, and the chart on the right shows the change in the F1\(c\) score. Regardless of the change in K value, the \(MLM+minit\) and \(GenTr+MLM+minit\) models consistently show better or similar performance than the baseline model (BERTs) in both F1 and F1\(c\). On the other hand, the \(GenTr\) and \(GenTr+MLM\) models outperformed the baseline performance when the K value was low in both F1 and F1\(c\), but as the K value increased, the increase was observed to be smaller than that of the baseline's performance. Based on these results, it can be recognized that leveraging the prompt-based technique along with deliberate initialization of inserted prompts ensures consistent performance across few-shot environments.