Extensively Matching for Few-shot Learning Event Detection
Table 3: Ablation study: F1-score (micro) of Prototypical-based models on dev set with 5+1-way 5-shot FSL setting
['Encoder', 'FSL Model', 'Original', '+Inter', '+Intra', '+Intra+Inter']
Besides the FSL formulation for event detection, a major contribution in this work involves the two loss functions Lintra and Linter to improve the representation vectors for the models. In particular, we focus on the prototypical-based FSL model on the 5+1-way 5-shot setting in this analysis (although the similar trends of the performance are also observed for the other models and settings). The “Original” column corresponds to the models where both Linter and Lintra are not applied. The other columns, on the other hand, report the performance of the models when the combinations Linter, Lintra, and Linter+Lintra of the loss terms are introduced.