论文标题
查询您的模型中的定义Framenet：框架语义角色标签的有效方法
Query Your Model with Definitions in FrameNet: An Effective Method for Frame Semantic Role Labeling
论文作者
论文摘要
框架语义角色标签（FSRL）标识参数，并用Framenet中定义的框架语义角色标记。先前的研究倾向于将FSRL分为参数识别和角色分类。这样的方法通常将角色分类模拟为幼稚的多类分类和分别对待参数，这忽略了标签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。在本文中，我们提出了一个名为“参数提取器”的基于查询的框架，该框架具有Framenet（老年）的定义，以减轻这些问题。 Framenet中框架和框架元素（FES）的定义可用于查询文本中的参数。编码文本定义对可以指导学习标签语义和加强论证相互作用的模型。实验表明，在两个Framenet数据集中，年龄较高的先前最先前的最先前的F1得分以及在零射击和几乎没有肖特场景中的衰老的概括能力。我们的代码和技术附录可从https://github.com/pkunlp-icler/aging获得。
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.
