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The semantic frame induction tasks are defined as a clustering of words intothe frames that they evoke, and a clustering of their arguments according tothe frame element roles that they should fill. In this paper, we address thelatter task of argument clustering, which aims to acquire frame elementknowledge, and propose a method that applies deep metric learning. In thismethod, a pre-trained language model is fine-tuned to be suitable fordistinguishing frame element roles through the use of frame-annotated data, andargument clustering is performed with embeddings obtained from the fine-tunedmodel. Experimental results on FrameNet demonstrate that our method achievessubstantially better performance than existing methods.

Acquiring Frame Element Knowledge with Deep Metric Learning for Semantic Frame Induction