| Challenge: | Few-shot domain adaptation and NOTA detection are two real-world challenges for few-shot relation classification models. |
| Approach: | They propose a task to investigate two aspects of few-shot relation classification models . they build upon the FewRel dataset by adding a new test set in a different domain . |
| Outcome: | The proposed task can evaluate few-shot domain adaptation and few- shot none-of-the-above detection on a new domain and NOTA relation choice. |
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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. |
Exploring the zero-shot limit of FewRel (2020.coling-main)
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| Challenge: | Existing methods to extract information from a language model are limited in their ability to generalize and do not perform as well as few-shot learning models. |
| Approach: | They propose a general purpose relation extractor that uses Wikidata descriptions to represent the relation’s surface form. |
| Outcome: | The proposed system is based on a FewRel 1.0 dataset, which provides an excellent framework for training and evaluating the proposed system in English. |
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. |
Revisiting Few-shot Relation Classification: Evaluation Data and Classification Schemes (2021.tacl-1)
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| Challenge: | a recent study has focused on few-shot learning (FSL) for relation classification, but it requires large amounts of training data. |
| Approach: | They propose a method for deriving more realistic few-shot test data from available datasets for supervised RC. |
| Outcome: | The proposed method yields a challenging benchmark for FSL-RC on which state of the art models show poor performance. |
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. |
Towards Robust Few-Shot Relation Classification: Incorporating Relation Description with Agreement (2025.findings-emnlp)
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Mengting Hu, Jianfeng Wu, Ming Jiang, Yalan Xie, Zhunheng Wang, Rui Ying, Xiaoyi Liu, Ruixuan Xu, Hang Gao, Renhong Cheng
| Challenge: | Existing approaches to recognize relational relationships with a few support samples are limited for unlimited queries. |
| Approach: | They propose a simple but effective framework that uses relation descriptions as external knowledge to enhance the model’s comprehension of the relation semantics. |
| Outcome: | The proposed framework outperforms strong baselines while being robust against various NOTA rates. |
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 . |
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Density-Aware Prototypical Network for Few-Shot Relation Classification (2023.findings-emnlp)
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| Challenge: | Existing studies treat NOTA as an extra class and treat it the same as known relations. |
| Approach: | They propose a density-aware prototypical network to treat various instances distinctly . they separate known instances and isolate NOTA instances, respectively . their code will be made public after the paper is accepted . |
| Outcome: | The proposed method outperforms strong baselines with robustness towards different NOTA rates. |
Few-NERD: A Few-shot Named Entity Recognition Dataset (2021.acl-long)
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| Challenge: | Existing approaches to few-shot named entity recognition (NER) focus on coarse-grained entities with few examples, while most unseen entities are fine-grounded. |
| Approach: | They present a human-annotated few-shot named entity recognition dataset . they construct benchmark tasks to assess the generalization capability of models . |
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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. |