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Semantic role labeling (SRL) is to recognize the predicate-argument structureof a sentence, including subtasks of predicate disambiguation and argumentlabeling. Previous studies usually formulate the entire SRL problem into two ormore subtasks. For the first time, this paper introduces an end-to-end neuralmodel which unifiedly tackles the predicate disambiguation and the argumentlabeling in one shot. Using a biaffine scorer, our model directly predicts allsemantic role labels for all given word pairs in the sentence without relyingon any syntactic parse information. Specifically, we augment the BiLSTM encoderwith a non-linear transformation to further distinguish the predicate and theargument in a given sentence, and model the semantic role labeling process as aword pair classification task by employing the biaffine attentional mechanism.Though the proposed model is syntax-agnostic with local decoder, it outperformsthe state-of-the-art syntax-aware SRL systems on the CoNLL-2008, 2009benchmarks for both English and Chinese. To our best knowledge, we report thefirst syntax-agnostic SRL model that surpasses all known syntax-aware models.

A Full End-to-End Semantic Role Labeler, Syntax-agnostic Over Syntax-aware?