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.

本文使用深度学习框架，通过实验探究了句法信息对神经语义角色标注 (SRL) 的重要性，发现在一定条件下，神经 SRL 模型仍能从句法信息中获益；并且，本文使用现有模型进行了全面的实证研究，量化了句法对神经 SRL 模型的重要性。

神经语义角色标注中的语法角色

Syntax Role for Neural Semantic Role Labeling

Semantic role labeling (SRL) is the task of identifying predicates and
labeling argument spans with semantic roles. Even though most semantic-role
formalisms are built upon constituent syntax and only syntactic constituents
can be labeled as arguments (e.g., FrameNet and PropBank), all the recent work
on syntax-aware SRL relies on dependency representations of syntax. In
contrast, we show how graph convolutional networks (GCNs) can be used to encode
constituent structures and inform an SRL system. Nodes in our SpanGCN
correspond to constituents. The computation is done in 3 stages. First, initial
node representations are produced by `composing' word representations of the
first and the last word in the constituent. Second, graph convolutions relying
on the constituent tree are performed, yielding syntactically-informed
constituent representations. Finally, the constituent representations are
`decomposed' back into word representations which in turn are used as input to
the SRL classifier. We evaluate SpanGCN against alternatives, including a model
using GCNs over dependency trees, and show its effectiveness on standard
CoNLL-2005, CoNLL-2012, and FrameNet benchmarks.

通过使用图卷积网络（GCNs）对成分结构进行编码，我们展示了如何使用 GCNs 来编码成分结构并在语义角色标注系统中实现句法信息。我们的 SpanGCN 通过使用 GCN 超过依赖树的模型进行评估，并在标准的 CoNLL-2005，CoNLL-2012 和 FrameNet 基准测试中证明了其有效性。