Semantic role labeling (SRL) is a task to recognize all the predicate-argument pairs of a sentence, which has been in a performance improvement bottleneck after a series of latest works were presented. This paper proposes a novel syntax-agnostic SRL model enhanced by the proposed associated memory network (AMN), which makes use of inter-sentence attention of label-known associated sentences as a kind of memory to further enhance dependency-based SRL. In detail, we use sentences and their labels from train dataset as an associated memory cue to help label the target sentence. Furthermore, we compare several associated sentences selecting strategies and label merging methods in AMN to find and utilize the label of associated sentences while attending them. By leveraging the attentive memory from known training data, Our full model reaches state-of-the-art on CoNLL-2009 benchmark datasets for syntax-agnostic setting, showing a new effective research line of SRL enhancement other than exploiting external resources such as well pre-trained language models.

本文提出了一种基于所提出的关联记忆网络的新颖的语法无关的语义角色标注模型，利用标签已知的相关句子的跨句子注意力作为一种记忆进一步增强基于依存的语义角色标注，并且通过利用已知训练数据的注意力记忆，我们的模型取得了CoNLL-2009基准数据集上的最新成果，在增强SR L方面展示了一个新的有效研究方向，而不是利用外部资源，如预先训练的语言模型。

关联记忆网络的语义角色标注