Zero-Shot Transfer Learning for Event Extraction
Table 5: Hit@K Performance on Trigger and Argument Classification.
['Setting', 'Method', 'Hit@k Trigger Classification (%) 1', 'Hit@k Trigger Classification (%) 3', 'Hit@k Trigger Classification (%) 5', 'Hit@k Argument Classification (%) 1', 'Hit@k Argument Classification (%) 3', 'Hit@k Argument Classification (%) 5']
To show the effectiveness of structural similarity in our approach, we design a baseline, WSD-Embedding, which directly grounds event mentions and arguments to their candidate types and roles using our pre-trained word sense embeddings. Also, as the number of seen types in training increases, the transfer model’s performance improves.