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