# A Simple yet Effective Relation Information Guided Approach for Few-Shot Relation Extraction

arXiv: https://arxiv.org/abs/2205.09536 · HTML: https://mlsanity.com/paper/2205.09536/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 21 references that are in the map.
- First posted on arXiv 19 May 2022.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Learning to Compare: Relation Network for Few-Shot Learning](https://mlsanity.com/paper/1711.06025/) (2017)
- [Few-Shot Learning with Graph Neural Networks](https://mlsanity.com/paper/1711.04043/) (2017)
- [Matching the Blanks: Distributional Similarity for Relation Learning](https://mlsanity.com/paper/1906.03158/) (2019)
- [Meta-Learning: A Survey](https://mlsanity.com/paper/1810.03548/) (2018)
- [FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation](https://mlsanity.com/paper/1810.10147/) (2018)
- [FewRel 2.0: Towards More Challenging Few-Shot Relation Classification](https://mlsanity.com/paper/1910.07124/) (2019)
- [Learning from Context or Names? An Empirical Study on Neural Relation Extraction](https://mlsanity.com/paper/2010.01923/) (2020)
- [Few-shot Relation Extraction via Bayesian Meta-learning on Relation Graphs](https://mlsanity.com/paper/2007.02387/) (2020)
- [Multi-Level Matching and Aggregation Network for Few-Shot Relation Classification](https://mlsanity.com/paper/1906.06678/) (2019)
- [Exploring Task Difficulty for Few-Shot Relation Extraction](https://mlsanity.com/paper/2109.05473/) (2021)
- [Word Graph Guided Summarization for Radiology Findings](https://mlsanity.com/paper/2112.09925/) (2021)
- [MapRE: An Effective Semantic Mapping Approach for Low-resource Relation Extraction](https://mlsanity.com/paper/2109.04108/) (2021)
- [Entity Concept-enhanced Few-shot Relation Extraction](https://mlsanity.com/paper/2106.02401/) (2021)

## Built on by (most cited first, 9 of 9)

- [Synergistic Anchored Contrastive Pre-training for Few-Shot Relation Extraction](https://mlsanity.com/paper/2312.12021/) (2023)
- [Linguistic representations for fewer-shot relation extraction across domains](https://mlsanity.com/paper/2307.03823/) (2023)
- [Knowledge-Enhanced Multi-Label Few-Shot Product Attribute-Value Extraction](https://mlsanity.com/paper/2308.08413/) (2023)
- [Generative Meta-Learning for Zero-Shot Relation Triplet Extraction](https://mlsanity.com/paper/2305.01920/) (2023)
- [Efficient Information Extraction in Few-Shot Relation Classification through Contrastive Representation Learning](https://mlsanity.com/paper/2403.16543/) (2024)
- [Few-Shot Joint Multimodal Entity-Relation Extraction via Knowledge-Enhanced Cross-modal Prompt Model](https://mlsanity.com/paper/2410.14225/) (2024)
- [WAVE++: Capturing Within-Task Variance for Continual Relation Extraction with Adaptive Prompting](https://mlsanity.com/paper/2505.13944/) (2025)
- [Few-Shot Relation Extraction with Hybrid Visual Evidence](https://mlsanity.com/paper/2403.00724/) (2024)
- [Few-shot Open Relation Extraction with Gaussian Prototype and Adaptive Margin](https://mlsanity.com/paper/2410.20320/) (2024)

## Abstract (arXiv)

Few-Shot Relation Extraction aims at predicting the relation for a pair of entities in a sentence by training with a few labelled examples in each relation. Some recent works have introduced relation information (i.e., relation labels or descriptions) to assist model learning based on Prototype Network. However, most of them constrain the prototypes of each relation class implicitly with relation information, generally through designing complex network structures, like generating hybrid features, combining with contrastive learning or attention networks. We argue that relation information can be introduced more explicitly and effectively into the model. Thus, this paper proposes a direct addition approach to introduce relation information. Specifically, for each relation class, the relation representation is first generated by concatenating two views of relations (i.e., [CLS] token embedding and the mean value of embeddings of all tokens) and then directly added to the original prototype for both train and prediction. Experimental results on the benchmark dataset FewRel 1.0 show significant improvements and achieve comparable results to the state-of-the-art, which demonstrates the effectiveness of our proposed approach. Besides, further analyses verify that the direct addition is a much more effective way to integrate the relation representations and the original prototypes.