DuRE: Dual Contrastive Self Training for Semi-Supervised Relation Extraction (2024.naacl-long)
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| Challenge: | Existing document-level relation extraction methods require manual training and labeled data to obtain supervised learning. |
| Approach: | They propose a document-level relation extraction framework that integrates RE and text generation as a dual process. |
| Outcome: | The proposed framework significantly boosts recall and F1 score with comparable precision on two document-level RE tasks against several strong baselines. |
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| Challenge: | Document-level Relation Extraction (DocRE) is a task that aims to extract relations from a long context. |
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| Challenge: | Existing methods only consider feature information of entity pairs, but our model exploits both feature information and previous predictions of entity pair. |
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TTM-RE: Memory-Augmented Document-Level Relation Extraction (2024.acl-long)
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| Challenge: | Existing methods for document-level relation extraction are ineffective in exploiting the full potential of large amounts of training data with varied noise levels. |
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DeepREF: A Framework for Optimized Deep Learning-based Relation Classification (2022.lrec-1)
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| Challenge: | Existing frameworks for relation extraction (RE) are limited due to lack of implementation details. |
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Continual Contrastive Finetuning Improves Low-Resource Relation Extraction (2023.acl-long)
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| Challenge: | Relation extraction (RE) has been challenging in low-resource domains and with limited resources. |
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Semi-supervised Relation Extraction via Incremental Meta Self-Training (2021.findings-emnlp)
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| Challenge: | Existing methods suffer from the gradual drift problem, where noisy pseudo labels are incorporated during training. |
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