Concretely, we first Then our attention mechanism models (trigger, context word) pair's relevance with a Multi-Layer Perceptron (MLP), and uses a softmax function normalizing relevance scores to attention weights: Given the attention weights, the lexical-specific context representation is summarized as c 0 = And the final lexical-specific representation of instance x is the concatenation of its token representation h 0 and the lexical-specific context representation c 0 , i.e., The lexical-specific representation can effectively disambiguate trigger words by capturing (trigger, context word) associations.
this representation is lexical-specific, thus hard to generalize well to sparse/unseen words.