Papers with representation learning paradigm

1 papers
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.
Approach: They propose to pretrain and finetune the RE model using consistent objectives of contrastive learning.
Outcome: The proposed method outperforms PLM-based RE classifier on two document-level RE datasets.

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