| Challenge: | Existing models follow a 'one-for-all' scheme where one general large model performs all individual N-way-K-shot tasks, which prevents the model from achieving the optimal point on each task. |
| Approach: | They propose a model generation framework that consists of one general model for all tasks and many tiny task-specific models for each individual task. |
| Outcome: | The proposed framework achieves state-of-the-art performance on two public datasets. |
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HyperNetwork-based Decoupling to Improve Model Generalization for Few-Shot Relation Extraction (2023.emnlp-main)
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| Challenge: | Existing studies cannot generalize well to unseen relations using Prototypical Networks . current approaches are dependent on large amount of labeled data and cannot deal with unseense relations well. |
| Approach: | They propose a HyperNetwork-based Decoupling approach to improve FSRE generalization . they propose FSre models with an encoder, network generator and refined classifiers . |
| Outcome: | The proposed method improves the generalization of few-shot relation extraction models. |
Exploring Task Difficulty for Few-Shot Relation Extraction (2021.emnlp-main)
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| Challenge: | Existing models do not distinguish hard tasks from easy ones in the learning process. |
| Approach: | They propose a novel approach that exploits relation label information to learn better representations by focusing on hard tasks. |
| Outcome: | Experiments on two standard datasets show the proposed approach performs better than previous methods. |
A Self-Denoising Model for Robust Few-Shot Relation Extraction (2025.acl-long)
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| Challenge: | Existing studies assume that the support set contains only accurately labeled instances, but this assumption is often unrealistic. |
| Approach: | They propose a self-denoising model for FSRE which can automatically correct noisy labels of support instances. |
| Outcome: | The proposed model outperforms all baselines on two public datasets showing that it can correct mislabeled support instances. |
Enhancing the Prototype Network with Local-to-Global Optimization for Few-Shot Relation Extraction (2025.findings-naacl)
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| Challenge: | Relation Extraction (RE) is a task that aims to extract semantic relationships from unstructured text. |
| Approach: | They propose a local optimization strategy that indirectly optimizes the prototypical networks by optimizing the other information contained within the prototypes. |
| Outcome: | The proposed model improves on the FewRel 1.0 and FewRela 2.0 datasets. |
Entity Concept-enhanced Few-shot Relation Extraction (2021.acl-short)
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| Challenge: | Existing FSRE methods fail to classify relations based on information of sentences and entity pairs due to limited samples and lack of knowledge. |
| Approach: | They propose a concept-sentence attention module to select the most appropriate concept from multiple concepts of each entity by calculating the semantic similarity between sentences and concepts. |
| Outcome: | The proposed scheme outperforms existing methods on a few-shot relation extraction dataset. |
Towards Realistic Few-Shot Relation Extraction (2021.emnlp-main)
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| Challenge: | Recent studies have shown that few-shot relation classification models can be used to extract any relation of interest from a collection of text with only a few example instances. |
| Approach: | They propose to modify the training routine to encourage models to better discriminate between relations involving similar entity types. |
| Outcome: | The proposed models outperform human models on relation extraction tasks while relying on entity type information. |
Towards Realistic Few-Shot Relation Extraction: A New Meta Dataset and Evaluation (2024.lrec-main)
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| Challenge: | Existing methods for few-shot relation extraction are not realistic due to the large amount of training data required. |
| Approach: | They propose a meta dataset for few-shot relation extraction based on existing supervised relation extraction datasets and a few-shot form of the TACRED dataset. |
| Outcome: | The proposed methods perform poorly on the few-shot relation extraction task. |
FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation (D18-1)
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| Challenge: | Empirical results show that even the most competitive few-shot learning models struggle on this task, especially as compared with humans. |
| Approach: | They propose a Few-Shot Relation Classification Dataset consisting of 70, 000 sentences on 100 relations derived from Wikipedia and annotated by crowdworkers. |
| Outcome: | The proposed methods perform well on the most competitive few-shot learning models, especially as compared with humans. |
GLiREL - Generalist Model for Zero-Shot Relation Extraction (2025.naacl-long)
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| Challenge: | Existing approaches to zero-shot named entity recognition rely on distant supervision and training data for unseen labels. |
| Approach: | They propose an efficient architecture and training paradigm for zero-shot relation classification . they use a protocol to generate multiple relation labels in a single forward pass . |
| Outcome: | The proposed architecture and training paradigm achieve state-of-the-art results on the zero-shot relation classification task. |
Learning to Generate Rules for Realistic Few-Shot Relation Classification: An Encoder-Decoder Approach (2024.findings-emnlp)
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| Challenge: | a new approach to relation classification is proposed to use data-driven approaches to perform fewshot tasks with limited training data. |
| Approach: | They propose a neuro-symbolic approach for realistic few-shot relation classification via rules . they propose to generate rules that can be used to extract relations using custom T5-style models . |
| Outcome: | The proposed approach is interpretable and pliable and outperforms the state-of-the-art on TACRED and NYT29 benchmarks while maintaining pliability. |