# Exploring Task Difficulty for Few-Shot Relation Extraction

arXiv: https://arxiv.org/abs/2109.05473 · HTML: https://mlsanity.com/paper/2109.05473/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 42 references that are in the map.
- First posted on arXiv 12 Sep 2021.

## Builds on (most cited first, 27 of 27)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [Learning to Compare: Relation Network for Few-Shot Learning](https://mlsanity.com/paper/1711.06025/) (2017)
- [A Survey on Contrastive Self-supervised Learning](https://mlsanity.com/paper/2011.00362/) (2020)
- [Few-Shot Learning with Graph Neural Networks](https://mlsanity.com/paper/1711.04043/) (2017)
- [Supervised Contrastive Learning for Pre-trained Language Model Fine-tuning](https://mlsanity.com/paper/2011.01403/) (2020)
- [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)
- [Retrieve, Program, Repeat: Complex Knowledge Base Question Answering via Alternate Meta-learning](https://mlsanity.com/paper/2010.15875/) (2020)
- [CERT: Contrastive Self-supervised Learning for Language Understanding](https://mlsanity.com/paper/2005.12766/) (2020)
- [End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures](https://mlsanity.com/paper/1601.00770/) (2016)
- [Reasoning with Latent Structure Refinement for Document-Level Relation Extraction](https://mlsanity.com/paper/2005.06312/) (2020)
- [FewRel 2.0: Towards More Challenging Few-Shot Relation Classification](https://mlsanity.com/paper/1910.07124/) (2019)
- [Interventional Video Grounding with Dual Contrastive Learning](https://mlsanity.com/paper/2106.11013/) (2021)
- [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)
- [Entity Concept-enhanced Few-shot Relation Extraction](https://mlsanity.com/paper/2106.02401/) (2021)
- [Expert Training: Task Hardness Aware Meta-Learning for Few-Shot Classification](https://mlsanity.com/paper/2007.06240/) (2020)
- [Speaker-Oriented Latent Structures for Dialogue-Based Relation Extraction](https://mlsanity.com/paper/2109.05182/) (2021)

## Built on by (most cited first, 24 of 24)

- [How to Unleash the Power of Large Language Models for Few-shot Relation Extraction?](https://mlsanity.com/paper/2305.01555/) (2023)
- [A Comprehensive Survey on Relation Extraction: Recent Advances and New Frontiers](https://mlsanity.com/paper/2306.02051/) (2023)
- [Unified Low-Resource Sequence Labeling by Sample-Aware Dynamic Sparse Finetuning](https://mlsanity.com/paper/2311.03748/) (2023)
- [Generative Prompt Tuning for Relation Classification](https://mlsanity.com/paper/2210.12435/) (2022)
- [Meta In-Context Learning Makes Large Language Models Better Zero and Few-Shot Relation Extractors](https://mlsanity.com/paper/2404.17807/) (2024)
- [A Simple yet Effective Relation Information Guided Approach for Few-Shot Relation Extraction](https://mlsanity.com/paper/2205.09536/) (2022)
- [Prompt-based Zero-shot Relation Extraction with Semantic Knowledge Augmentation](https://mlsanity.com/paper/2112.04539/) (2021)
- [Better Few-Shot Relation Extraction with Label Prompt Dropout](https://mlsanity.com/paper/2210.13733/) (2022)
- [Generator-Retriever-Generator Approach for Open-Domain Question Answering](https://mlsanity.com/paper/2307.11278/) (2023)
- [Synergistic Anchored Contrastive Pre-training for Few-Shot Relation Extraction](https://mlsanity.com/paper/2312.12021/) (2023)
- [Knowledge-Enhanced Multi-Label Few-Shot Product Attribute-Value Extraction](https://mlsanity.com/paper/2308.08413/) (2023)
- [Robust Text Classification: Analyzing Prototype-Based Networks](https://mlsanity.com/paper/2311.06647/) (2023)
- [Few-shot Classification with Hypersphere Modeling of Prototypes](https://mlsanity.com/paper/2211.05319/) (2022)
- [EnCore: Fine-Grained Entity Typing by Pre-Training Entity Encoders on Coreference Chains](https://mlsanity.com/paper/2305.12924/) (2023)
- [CORE: A Few-Shot Company Relation Classification Dataset for Robust Domain Adaptation](https://mlsanity.com/paper/2310.12024/) (2023)
- [Reconciliation of Pre-trained Models and Prototypical Neural Networks in Few-shot Named Entity Recognition](https://mlsanity.com/paper/2211.03270/) (2022)
- [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)
- [TPN: Transferable Proto-Learning Network towards Few-shot Document-Level Relation Extraction](https://mlsanity.com/paper/2410.00412/) (2024)
- [Few-Shot Joint Multimodal Entity-Relation Extraction via Knowledge-Enhanced Cross-modal Prompt Model](https://mlsanity.com/paper/2410.14225/) (2024)
- [Relation Extraction Across Entire Books to Reconstruct Community Networks: The AffilKG Datasets](https://mlsanity.com/paper/2505.10798/) (2025)
- [RAPS: A Novel Few-Shot Relation Extraction Pipeline with Query-Information Guided Attention and Adaptive Prototype Fusion](https://mlsanity.com/paper/2210.08242/) (2022)
- [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 (FSRE) focuses on recognizing novel relations by learning with merely a handful of annotated instances. Meta-learning has been widely adopted for such a task, which trains on randomly generated few-shot tasks to learn generic data representations. Despite impressive results achieved, existing models still perform suboptimally when handling hard FSRE tasks, where the relations are fine-grained and similar to each other. We argue this is largely because existing models do not distinguish hard tasks from easy ones in the learning process. In this paper, we introduce a novel approach based on contrastive learning that learns better representations by exploiting relation label information. We further design a method that allows the model to adaptively learn how to focus on hard tasks. Experiments on two standard datasets demonstrate the effectiveness of our method.