Semantic Frame Induction with Deep Metric Learning

Abstract

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 tackle supervised semantic frame induction, which assumes the existence of frame-annotated data for a subset of verbs in a corpus and propose to fine-tune contextualized word embedding models using deep metric learning for high-performance semantic frame induction methods. Our experiments on FrameNet show that fine-tuning with deep metric learning considerably improves the clustering evaluation scores by about 8 points or more. We also demonstrate that our approach is effective even when the number of training instances is small.