基于Self-Attention的句法感知汉语框架语义角色标注

Syntax-Aware Chinese Frame Semantic Role Labeling Based on Self-Attention

框架语义角色标注(Frame Semantic Role Labeling,FSRL)是基于FrameNet标注体系的语义分析任务。语义角色标注通常对句法有很强的依赖性,目前的语义角色标注模型大多基于双向长短时记忆网络Bi-LSTM,虽然可以获取句子中的长距离依赖信息,但无法很好地获取句子中的句法信息。因此,引入Self-Attention机制来捕获句子中每个词的句法信息。实验结果表明,该模型在CFN(Chinese FrameNet,汉语框架网)数据集上的F_(1)值得到了提升,证明了融入self-attention机制可以改进汉语框架语义角色标注模型的性能。

Frame semantic role labeling is a semantic analysis task based on theFrameNet.Semantic role labeling usually has a strong dependence on syntax.Most of the current semantic role labeling models are based on Bi-LSTM,which can obtain the long-distance dependency information in sentences,but cannot obtain the syntactic information in sentences well.In this paper,we iimental results show that the F_(1) of the model on the CFN(Chinese FrameNet)dataset has been improved,which proves that the self-attention mechanism can improve the performance of the Chinese frame semantic role labeling model.