Acquiring Frame Element Knowledge with Deep Metric Learning for Semantic Frame Induction
This work addresses the task of acquiring frame element knowledge for natural language processing, representing an incremental improvement in a domain-specific area.
The paper tackled the problem of argument clustering for semantic frame induction by proposing a deep metric learning method that fine-tunes a pre-trained language model to distinguish frame element roles, achieving substantially better performance than existing methods on FrameNet.
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.