Papers with few-shot relation classification
Efficient Information Extraction in Few-Shot Relation Classification through Contrastive Representation Learning (2024.naacl-short)
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| Challenge: | Relation classification (RC) models extract rich information from sentences with limited labeled instances. |
| Approach: | They propose to combine multiple sentence representations with contrastive learning to enhance information extraction by combining multiple sentence and entity tokens. |
| Outcome: | The proposed approach is able to extract discriminative information from multiple representations and contrastive learning. |
Meta-Information Guided Meta-Learning for Few-Shot Relation Classification (2020.coling-main)
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| Challenge: | Existing meta-learning models rely on implicit instance statistics and are unreliability and weak interpretability. |
| Approach: | They propose a meta-information guided meta-learning framework that uses semantics to guide meta- learning . experimental results demonstrate the effectiveness of the proposed framework . |
| Outcome: | The proposed framework can establish connections between instance-based information and semantic-based data, enabling faster initialization and adaptation. |
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. |
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. |
Multi-Level Matching and Aggregation Network for Few-Shot Relation Classification (P19-1)
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| Challenge: | Existing methods for few-shot relation classification use supervised training, but lack of large-scale manually labeled data. |
| Approach: | They propose a multi-level matching and aggregation network (MLMAN) for few-shot relation classification. |
| Outcome: | The proposed model achieves state-of-the-art performance on the FewRel dataset. |
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. |
| Outcome: | The proposed model achieves comparable and even better results to strong baselines in terms of accuracy. |
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. |