Extensively Matching for Few-shot Learning Event Detection
Table 1: F1-score (micro) of models using CNN, LSTM and GCN encoders without proposed losses.
['Model Encoder', '5+1-way 5-shot CNN', '5+1-way 5-shot LSTM', '5+1-way 5-shot GCNN', '10+1-way 10-shot CNN', '10+1-way 10-shot LSTM', '10+1-way 10-shot GCNN']
Second, we can see that Prototypical-based (Proto and Proto+Att) models outperform the Matching network and the Relation network on both FSL settings. Among Prototypical network models, Proto+Att is slightly better than Proto with a 0.8% performance gap in the 10+1-way 10-shot setting.