  Semantic Role Labeling (SRL) is believed to be a crucial step towards natural
language understanding and has been widely studied. Recent years, end-to-end
SRL with recurrent neural networks (RNN) has gained increasing attention.
However, it remains a major challenge for RNNs to handle structural information
and long range dependencies. In this paper, we present a simple and effective
architecture for SRL which aims to address these problems. Our model is based
on self-attention which can directly capture the relationships between two
tokens regardless of their distance. Our single model achieves F$_1=83.4$ on
the CoNLL-2005 shared task dataset and F$_1=82.7$ on the CoNLL-2012 shared task
dataset, which outperforms the previous state-of-the-art results by $1.8$ and
$1.0$ F$_1$ score respectively. Besides, our model is computationally
efficient, and the parsing speed is 50K tokens per second on a single Titan X
GPU.
