# MORE: A Metric Learning Based Framework for Open-domain Relation Extraction

arXiv: https://arxiv.org/abs/2206.00289 · HTML: https://mlsanity.com/paper/2206.00289/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 27 references that are in the map.
- First posted on arXiv 1 Jun 2022.

## 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)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning](https://mlsanity.com/paper/1704.03976/) (2017)
- [Deep metric learning using Triplet network](https://mlsanity.com/paper/1412.6622/) (2014)
- [Matching the Blanks: Distributional Similarity for Relation Learning](https://mlsanity.com/paper/1906.03158/) (2019)
- [FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation](https://mlsanity.com/paper/1810.10147/) (2018)
- [No Fuss Distance Metric Learning using Proxies](https://mlsanity.com/paper/1703.07464/) (2017)
- [Ranked List Loss for Deep Metric Learning](https://mlsanity.com/paper/1903.03238/) (2019)
- [SelfORE: Self-supervised Relational Feature Learning for Open Relation Extraction](https://mlsanity.com/paper/2004.02438/) (2020)
- [Neural Snowball for Few-Shot Relation Learning](https://mlsanity.com/paper/1908.11007/) (2019)
- [Unsupervised Open Relation Extraction](https://mlsanity.com/paper/1801.07174/) (2018)

## Built on by (most cited first, 5 of 5)

- [MUFFIN: Curating Multi-Faceted Instructions for Improving Instruction-Following](https://mlsanity.com/paper/2312.02436/) (2023)
- [Improving Unsupervised Relation Extraction by Augmenting Diverse Sentence Pairs](https://mlsanity.com/paper/2312.00552/) (2023)
- [Do You Hear The People Sing? Key Point Analysis via Iterative Clustering and Abstractive Summarisation](https://mlsanity.com/paper/2305.16000/) (2023)
- [Toward Zero-Shot Instruction Following](https://mlsanity.com/paper/2308.03795/) (2023)
- [TPN: Transferable Proto-Learning Network towards Few-shot Document-Level Relation Extraction](https://mlsanity.com/paper/2410.00412/) (2024)

## Abstract (arXiv)

Open relation extraction (OpenRE) is the task of extracting relation schemes from open-domain corpora. Most existing OpenRE methods either do not fully benefit from high-quality labeled corpora or can not learn semantic representation directly, affecting downstream clustering efficiency. To address these problems, in this work, we propose a novel learning framework named MORE (Metric learning-based Open Relation Extraction). The framework utilizes deep metric learning to obtain rich supervision signals from labeled data and drive the neural model to learn semantic relational representation directly. Experiments result in two real-world datasets show that our method outperforms other state-of-the-art baselines. Our source code is available on Github.