Extensively Matching for Few-shot Learning Event Detection
Table 2: F1-score (micro) of models using CNN, LSTM, and GCN. Original columns show the models without additional training signal. Linter+Lintra columns demonstrate the models with additional inter and intra loss functions.
['Encoder', 'Model', '5+1-way 5-shot Original', '5+1-way 5-shot +  [ITALIC] Linter+ [ITALIC] Lintra', '10+1-way 10-shot Original', '10+1-way 10-shot + [ITALIC] Linter+ [ITALIC] Lintra']
As we can see from the table, the proposed loss functions can significantly improve the performance of Proto and Proto+Att models over different encoders (i.e., CNN, LSTM, and GCN), clearly demonstrating the benefits of the intra and inter-similarity constraints in this work.