  End-to-end semantic role labeling (SRL) has been received increasing
interest. It performs the two subtasks of SRL: predicate identification and
argument role labeling, jointly. Recent work is mostly focused on graph-based
neural models, while the transition-based framework with neural networks which
has been widely used in a number of closely-related tasks, has not been studied
for the joint task yet. In this paper, we present the first work of
transition-based neural models for end-to-end SRL. Our transition model
incrementally discovers all sentential predicates as well as their arguments by
a set of transition actions. The actions of the two subtasks are executed
mutually for full interactions. Besides, we suggest high-order compositions to
extract non-local features, which can enhance the proposed transition model
further. Experimental results on CoNLL09 and Universal Proposition Bank show
that our final model can produce state-of-the-art performance, and meanwhile
keeps highly efficient in decoding. We also conduct detailed experimental
analysis for a deep understanding of our proposed model.
