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. |
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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. |
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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. |
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Pre-training to Match for Unified Low-shot Relation Extraction (2022.acl-long)
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| Challenge: | Low-shot relation extraction (RE) aims to recognize novel relations with very few or even no samples. |
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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. |
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