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Found 4 papers, 3 papers with code
Key Mention Pairs Guided Document-Level Relation Extraction
To this end, we propose a novel DocRE model called Key Mention pairs Guided Relation Extractor (KMGRE) to directly model mention-level relations, containing two modules: a mention-level relation extractor and a key instance classifier.
CETA: A Consensus Enhanced Training Approach for Denoising in Distantly Supervised Relation Extraction
This paper proposes a sentence-level DSRE method beyond typical instance selection approaches by preventing samples from falling into the wrong classification space on the feature space.
Explore Better Relative Position Embeddings from Encoding Perspective for Transformer Models
Relative position embedding (RPE) is a successful method to explicitly and efficaciously encode position information into Transformer models.
DAT: Training Deep Networks Robust To Label-Noise by Matching the Feature Distributions
Firstly, methods based on the noise probability modeling can only be applied to class-level noisy labels.
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