Fine-grained Contrastive Learning for Relation Extraction

Understand

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.