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 show the effectiveness of our syntax-aware model 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。

利用组成树的图卷积进行句法感知的语义角色标注