  Semantic role labeling (SRL) aims to identify the predicate-argument
structure of a sentence. Inspired by the strong correlation between syntax and
semantics, previous works pay much attention to improve SRL performance on
exploiting syntactic knowledge, achieving significant results. Pipeline methods
based on automatic syntactic trees and multi-task learning (MTL) approaches
using standard syntactic trees are two common research orientations. In this
paper, we adopt a simple unified span-based model for both span-based and
word-based Chinese SRL as a strong baseline. Besides, we present a MTL
framework that includes the basic SRL module and a dependency parser module.
Different from the commonly used hard parameter sharing strategy in MTL, the
main idea is to extract implicit syntactic representations from the dependency
parser as external inputs for the basic SRL model. Experiments on the
benchmarks of Chinese Proposition Bank 1.0 and CoNLL-2009 Chinese datasets show
that our proposed framework can effectively improve the performance over the
strong baselines. With the external BERT representations, our framework
achieves new state-of-the-art 87.54 and 88.5 F1 scores on the two test data of
the two benchmarks, respectively. In-depth analysis are conducted to gain more
insights on the proposed framework and the effectiveness of syntax.
