Zero-shot event argument extraction by disentangling trigger from argument and role

Event argument extraction (EAE) is an important task in information extraction. Traditional supervised methods are not easy to generalize to new event types. Most zero-shot EAE methods learned event-specific argument representation or event-specific role representation, which makes it difficult to generalize to new event types. We propose Distar, a zero-shot EAE method that disentangles the trigger from the argument and the role by modeling them using a knowledge graph embedding algorithm, i.e., TransE. We treat the argument as the head entity, the role as the relation, and the trigger as the tail entity. To enhance the generalization capacity, we construct two classifiers to relate the new role to the existing ontology. The experimental results show that our method can outperform the baseline by 8% on the ACE-2005 dataset. Our code is open-sourced at https://github.com/ZeroNLP/Distar for reproduction purposes.