∆-representation learning aims to decouple, learn, and fuse alterable ∆parts for event representation, instead of learning a single comprehensive representation.
we decouple an event representation r ed into three parts r ed = r w r d r g (Section 2), where r w is the pre-trained word representation of trigger words, r d is the lexical-specific event representation which captures discrimination knowledge for distinguishing ambiguous triggers, r g is the lexical-free event representation which captures generalization knowledge for detecting unseen/sparse triggers, and is the fusion function to fuse different parts.