# Semantic Frame Induction with Deep Metric Learning

arXiv: https://arxiv.org/abs/2304.14286 · HTML: https://mlsanity.com/paper/2304.14286/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 32 references that are in the map.
- First posted on arXiv 27 Apr 2023.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [CosFace: Large Margin Cosine Loss for Deep Face Recognition](https://mlsanity.com/paper/1801.09414/) (2018)
- [SphereFace: Deep Hypersphere Embedding for Face Recognition](https://mlsanity.com/paper/1704.08063/) (2017)
- [A Metric Learning Reality Check](https://mlsanity.com/paper/2003.08505/) (2020)
- [AdaCos: Adaptively Scaling Cosine Logits for Effectively Learning Deep Face Representations](https://mlsanity.com/paper/1905.00292/) (2019)
- [Frame-Semantic Parsing with Softmax-Margin Segmental RNNs and a Syntactic Scaffold](https://mlsanity.com/paper/1706.09528/) (2017)
- [Semantic Frame Induction using Masked Word Embeddings and Two-Step Clustering](https://mlsanity.com/paper/2105.13466/) (2021)
- [HHMM at SemEval-2019 Task 2: Unsupervised Frame Induction using Contextualized Word Embeddings](https://mlsanity.com/paper/1905.01739/) (2019)
- [Verb Sense Clustering using Contextualized Word Representations for Semantic Frame Induction](https://mlsanity.com/paper/2105.13465/) (2021)

## Built on by (most cited first, 1 of 1)

- [Acquiring Frame Element Knowledge with Deep Metric Learning for Semantic Frame Induction](https://mlsanity.com/paper/2305.13944/) (2023)

## Abstract (arXiv)

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.