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.

本文提出了第一个针对端到端语义角色标注任务采用转移模型的神经网络方法，并使用高阶组合提取非局部特征，实现了最佳性能和高效解码。在 CoNLL09 和 Universal Proposition Bank 上的实验结果表明，该方法可以创造最新的性能记录。

基于神经转移模型的端到端语义角色标注

End-to-end Semantic Role Labeling with Neural Transition-based Model

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.

本文提出一种统一的、端到端的模型，用于同时处理依存和跨度语义角色标注任务，并使用统一的参数学习预测谓词和论元。在两种语义表示形式中，本模型实现了新的最先进的结果。