Papers with few-shot relation extraction
Partial Order-centered Hyperbolic Representation Learning for Few-shot Relation Extraction (2025.coling-main)
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| Challenge: | Existing methods for few-shot relation extraction are limited to labeled instances and rely on data labeling. |
| Approach: | They propose a partial order-centered hyperbolic representation learning framework which imposes constraints on relations on instances by modeling partial order in hyperbolical space. |
| Outcome: | The proposed framework outperforms baseline methods on three benchmark datasets on 1-shot settings lacking relation descriptions. |
Learn from Relation Information: Towards Prototype Representation Rectification for Few-Shot Relation Extraction (2022.findings-naacl)
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| Challenge: | Existing methods for Few-shot Relation Extraction focus on implicitly introducing relation information to constrain the prototype representation learning. |
| Approach: | They propose a parameter-less method to promote few-shot relation extraction . they use a prototype rectification module to rectify original prototypes by relation information . |
| Outcome: | The proposed method achieves state-of-the-art on fewRel 1.0 and 2.0 datasets. |
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. |
Chain of Thought with Explicit Evidence Reasoning for Few-shot Relation Extraction (2023.findings-emnlp)
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| Challenge: | Existing approaches to few-shot relation extraction require training. |
| Approach: | They propose a method for few-shot relation extraction using large language models, called CoT-ER, chain-of-thought with explicit evidence reasoning. |
| Outcome: | The proposed approach achieves competitive performance compared to the fully-supervised state-of-the-art approach on the FewRel1.0 and FewRela2.0 datasets. |
DRK: Discriminative Rule-based Knowledge for Relieving Prediction Confusions in Few-shot Relation Extraction (2022.coling-1)
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| Challenge: | Existing methods to identify relation type in low-resource scenario fall into prediction confusions owing to the limited inference ability over shallow text features. |
| Approach: | They propose a discriminative rule-based knowledge method to identify the relation type between entities in a given text in the low-resource scenario. |
| Outcome: | The proposed method improves on four types of meta tasks with a 6.0% accuracy gain on average. |
Few-Shot Relation Extraction with Hybrid Visual Evidence (2024.lrec-main)
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| Challenge: | Existing few-shot relation extraction methods focus on uni-modal information such as text only. Existing methods focus only on text, requiring only a few labeled instances for training. |
| Approach: | They propose a multi-modal few-shot relation extraction model that leverages both textual and visual semantic information to learn a multiple-modal representation jointly. |
| Outcome: | The proposed model leverages both textual and visual semantic information to learn a multi-modal representation jointly. |
GRADUAL: Granularity-aware Dual Prototype Learning for Better Few-Shot Relation Extraction (2024.findings-acl)
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| Challenge: | Existing methods for few-shot relation extraction use text labels and context sentences to learn prototype representations. |
| Approach: | They propose a "dual prototype learning method" that integrates text labels and context sentences into prototype representations. |
| Outcome: | The proposed method achieves state-of-the-art performance in few-shot relation extraction. |
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. |