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.

本文介绍了基于统一覆盖模型的跨度和基于单词的语义角色标注，并提出了一种多任务学习框架，其中包括基本 SRL 模块和依赖性解析器模块，通过该框架，我们使用外部句法表示得到了新的最先进的中文 SRL 良好结果。