Query Your Model with Definitions in FrameNet: An Effective Method for Frame Semantic Role Labeling
This work addresses a specific bottleneck in natural language processing for tasks requiring semantic role labeling, offering incremental improvements in accuracy and generalization.
The paper tackles the problem of Frame Semantic Role Labeling (FSRL) by proposing a query-based framework that uses definitions from FrameNet to improve argument identification and role classification, resulting in a performance gain of up to 1.3 F1-score over previous state-of-the-art methods on two datasets.
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.