  Semantic role labeling (SRL) is dedicated to recognizing the semantic
predicate-argument structure of a sentence. Previous studies in terms of
traditional models have shown syntactic information can make remarkable
contributions to SRL performance; however, the necessity of syntactic
information was challenged by a few recent neural SRL studies that demonstrate
impressive performance without syntactic backbones and suggest that syntax
information becomes much less important for neural semantic role labeling,
especially when paired with recent deep neural network and large-scale
pre-trained language models. Despite this notion, the neural SRL field still
lacks a systematic and full investigation on the relevance of syntactic
information in SRL, for both dependency and both monolingual and multilingual
settings. This paper intends to quantify the importance of syntactic
information for neural SRL in the deep learning framework. We introduce three
typical SRL frameworks (baselines), sequence-based, tree-based, and
graph-based, which are accompanied by two categories of exploiting syntactic
information: syntax pruning-based and syntax feature-based. Experiments are
conducted on the CoNLL-2005, 2009, and 2012 benchmarks for all languages
available, and results show that neural SRL models can still benefit from
syntactic information under certain conditions. Furthermore, we show the
quantitative significance of syntax to neural SRL models together with a
thorough empirical survey using existing models.
