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

• URL: http://arxiv.org/abs/2305.13944v1
• Date: Tue, 23 May 2023 11:02:28 GMT
• Title: Acquiring Frame Element Knowledge with Deep Metric Learning for Semantic Frame Induction
• Authors: Kosuke Yamada, Ryohei Sasano, Koichi Takeda
• Abstract summary: We propose a method that applies deep metric learning to semantic frame induction tasks. A pre-trained language model is fine-tuned to be suitable for distinguishing frame element roles. Experimental results on FrameNet demonstrate that our method achieves substantially better performance than existing methods.
• Score: 24.486546938073907
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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