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(CoNLL , 2012 and dependency (CoNLL 2008(CoNLL , 2009) SRL benchmarks.
REF also proposed an endto-end model for both dependency and span SRL with a unified argument representation, obtaining favorable results on English.
Dependency or Span, End-to-End Uniform Semantic Role Labeling
ArXiv
ArXiv
Computer Science