Recent models have shown that incorporating syntactic knowledge into the semantic role labelling (SRL) task leads to a significant improvement. In this paper, we propose Syntax-aware Graph-to-Graph Transformer (SynG2G-Tr) model, which encodes the syntactic structure using a novel way to input graph relations as embeddings, directly into the self-attention mechanism of Transformer. This approach adds a soft bias towards attention patterns that follow the syntactic structure but also allows the model to use this information to learn alternative patterns. We evaluate our model on both span-based and dependency-based SRL datasets, and outperform previous alternative methods in both in-domain and out-of-domain settings, on CoNLL 2005 and CoNLL 2009 datasets.

翻译：近期模型研究表明，将句法知识融入语义角色标注任务可显著提升性能。本文提出语法感知图到图Transformer模型（SynG2G-Tr），通过将图关系以嵌入形式直接输入Transformer的自注意力机制，创新性地对句法结构进行编码。该方法在为遵循句法结构的注意力模式引入软偏置的同时，允许模型利用此类信息学习替代性注意力模式。我们在基于跨度与基于依存关系的两类语义角色标注数据集上评估该模型，在CoNLL 2005与CoNLL 2009数据集的域内及跨域场景中均优于现有方法。