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Found 4 papers, 1 papers with code
On Compositionality and Improved Training of NADO
NeurAlly-Decomposed Oracle (NADO) is a powerful approach for controllable generation with large language models.
A Practical Framework for Relation Extraction with Noisy Labels Based on Doubly Transitional Loss
One transition is basically parameterized by a non-linear transformation between hidden layers that implicitly represents the conversion between the true and noisy labels, and it can be readily optimized together with other model parameters.
Enriching Pre-trained Language Model with Entity Information for Relation Classification
In this paper, we propose a model that both leverages the pre-trained BERT language model and incorporates information from the target entities to tackle the relation classification task.
Ranked #15 on
Relation Extraction
on SemEval-2010 Task-8
Improving Distantly Supervised Relation Extraction with Neural Noise Converter and Conditional Optimal Selector
Distant supervised relation extraction has been successfully applied to large corpus with thousands of relations.
