Frame Semantic Role Labeling (FSRL) identifies arguments and labels them with frame semantic roles defined in FrameNet. Previous researches tend to divide FSRL into argument identification and role classification. Such methods usually model role classification as naive multi-class classification and treat arguments individually, which neglects label semantics and interactions between arguments and thus hindering performance and generalization of models. In this paper, we propose a query-based framework named ArGument Extractor with Definitions in FrameNet (AGED) to mitigate these problems. Definitions of frames and frame elements (FEs) in FrameNet can be used to query arguments in text. Encoding text-definition pairs can guide models in learning label semantics and strengthening argument interactions. Experiments show that AGED outperforms previous state-of-the-art by up to 1.3 F1-score in two FrameNet datasets and the generalization power of AGED in zero-shot and fewshot scenarios. Our code and technical appendix is available at https://github.com/PKUnlp-icler/AGED.
翻译:框架语义作用标签( FSRL ), 以框架 Net 定义的语义作用来辨别参数和标签。 以前的研究往往将 FSRL 划分为参数识别和角色分类。 这种方法通常以天真的多级分类和单独处理参数为示范角色分类, 忽视了语义和参数之间的相互作用, 从而阻碍了模型的性能和概括化。 在本文中, 我们提议了一个名为ArGument Exportor 的查询框架网络定义( AGED ) 的框架框架网络中的参数和框架元素( FES ) 来缓解这些问题。 FramNet 中的框架和框架元素( FES) 定义可以用于在文本中查询参数。 编码文本定义的文本定义配对可以指导在学习标签语义和强化参数互动方面的模型。 实验显示, AGEGED 在两个框架网数据集中, 超越了先前的状态, 最高为1.3 F1- 核心, 在零点和小片段情景中, AGED 。 我们的代码和技术附录可在 https://github.com/ PKUnlp- icler/ AGEGedD 。