  Semantic role labeling (SRL) aims to discover the predicateargument structure
of a sentence. End-to-end SRL without syntactic input has received great
attention. However, most of them focus on either span-based or dependency-based
semantic representation form and only show specific model optimization
respectively. Meanwhile, handling these two SRL tasks uniformly was less
successful. This paper presents an end-to-end model for both dependency and
span SRL with a unified argument representation to deal with two different
types of argument annotations in a uniform fashion. Furthermore, we jointly
predict all predicates and arguments, especially including long-term ignored
predicate identification subtask. Our single model achieves new
state-of-the-art results on both span (CoNLL 2005, 2012) and dependency (CoNLL
2008, 2009) SRL benchmarks.
