Acquiring Frame Element Knowledge with Deep Metric Learning for Semantic
  Frame Induction
Kosuke Yamada Ryohei Sasano Koichi Takeda
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.