  Recent studies have demonstrated the usefulness of contextualized word
embeddings in unsupervised semantic frame induction. However, they have also
revealed that generic contextualized embeddings are not always consistent with
human intuitions about semantic frames, which causes unsatisfactory performance
for frame induction based on contextualized embeddings. In this paper, we
address supervised semantic frame induction, which assumes the existence of
frame-annotated data for a subset of predicates in a corpus and aims to build a
frame induction model that leverages the annotated data. We propose a model
that uses deep metric learning to fine-tune a contextualized embedding model,
and we apply the fine-tuned contextualized embeddings to perform semantic frame
induction. Our experiments on FrameNet show that fine-tuning with deep metric
learning considerably improves the clustering evaluation scores, namely, the
B-cubed F-score and Purity F-score, by about 8 points or more. We also
demonstrate that our approach is effective even when the number of training
instances is small.
