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. |
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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. |
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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. |
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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. |
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FewRel 2.0: Towards More Challenging Few-Shot Relation Classification (D19-1)
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| 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 . |
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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. |
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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Learning to Decouple Relations: Few-Shot Relation Classification with Entity-Guided Attention and Confusion-Aware Training (2020.coling-main)
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| Challenge: | Existing few-shot relation classifiers struggle to distinguish them with few annotated instances due to high co-occurrence of some relations . |
| Approach: | They propose a few-shot relation classification model with two mechanisms to decouple easily-confused relations. |
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Dependency-aware Prototype Learning for Few-shot Relation Classification (2022.coling-1)
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| Challenge: | Existing methods for few-shot relation classification fail to distinguish multiple relations that co-exist in one sentence. |
| Approach: | They propose a dependency-aware prototype learning method for few-shot relation classification . they utilize dependency trees and shortest dependency paths as structural information . |
| Outcome: | The proposed method achieves better performance than baselines on the FewRel dataset. |
A Simple yet Effective Relation Information Guided Approach for Few-Shot Relation Extraction (2022.findings-acl)
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| Challenge: | Existing approaches to introduce relation information into the model are limited by labeling and data scarcity. |
| Approach: | They propose a direct addition approach to introduce relation information into a model by concatenating two views of relations and adding them to the original prototype. |
| Outcome: | The proposed approach improves on the benchmark dataset FewRel 1.0 and shows comparable results to the state-of-the-art. |
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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