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

Autor:	Yamada, Kosuke, Sasano, Ryohei, Takeda, Koichi
Rok vydání:	2023
Předmět:	Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	The semantic frame induction tasks are defined as a clustering of words into the frames that they evoke, and a clustering of their arguments according to the frame element roles that they should fill. In this paper, we address the latter task of argument clustering, which aims to acquire frame element knowledge, and propose a method that applies deep metric learning. In this method, a pre-trained language model is fine-tuned to be suitable for distinguishing frame element roles through the use of frame-annotated data, and argument clustering is performed with embeddings obtained from the fine-tuned model. Experimental results on FrameNet demonstrate that our method achieves substantially better performance than existing methods. Comment: Findings of ACL 2023
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2305.13944 Zobrazit plný text záznamu View this record from Arxiv