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Semantic role labeling (SRL) aims to discover the predicateargument structureof a sentence. End-to-end SRL without syntactic input has received greatattention. However, most of them focus on either span-based or dependency-basedsemantic representation form and only show specific model optimizationrespectively. Meanwhile, handling these two SRL tasks uniformly was lesssuccessful. This paper presents an end-to-end model for both dependency andspan SRL with a unified argument representation to deal with two differenttypes of argument annotations in a uniform fashion. Furthermore, we jointlypredict all predicates and arguments, especially including long-term ignoredpredicate identification subtask. Our single model achieves newstate-of-the-art results on both span (CoNLL 2005, 2012) and dependency (CoNLL2008, 2009) SRL benchmarks.

Dependency or Span, End-to-End Uniform Semantic Role Labeling