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Title: Fine-grained Contrastive Learning for Relation Extraction
Recent relation extraction (RE) works have shown encouraging improvements by conducting contrastive learning on silver labels generated by distant supervision before fine-tuning on gold labels. Existing methods typically assume all these silver labels are accurate and treat them equally; however, distant supervision is inevitably noisy–some silver labels are more reliable than others. In this paper, we propose fine-grained contrastive learning (FineCL) for RE, which leverages fine-grained information about which silver labels are and are not noisy to improve the quality of learned relationship representations for RE. We first assess the quality of silver labels via a simple and automatic approach we call “learning order denoising,” where we train a language model to learn these relations and record the order of learned training instances. We show that learning order largely corresponds to label accuracy–early-learned silver labels have, on average, more accurate labels than later-learned silver labels. Then, during pre-training, we increase the weights of accurate labels within a novel contrastive learning objective. Experiments on several RE benchmarks show that FineCL makes consistent and significant performance gains over state-of-the-art methods.  more » « less
Award ID(s):
2040727
NSF-PAR ID:
10403510
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Page Range / eLocation ID:
1083–1095
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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