Search Results for author:
Found 3 papers, 1 papers with code
Generative Meta-Learning for Zero-Shot Relation Triplet Extraction
However, current generative models lack the optimization process of model generalization on different tasks during training, and thus have limited generalization capability.
From Consensus to Disagreement: Multi-Teacher Distillation for Semi-Supervised Relation Extraction
Specifically, we first let the teachers correspond to the multiple models and select the samples in the intersection set of the last iteration in SSRE methods to augment labeled data as usual.
Exploit Multiple Reference Graphs for Semi-supervised Relation Extraction
To tackle this limitation, we propose to build the connection between the unlabeled data and the labeled ones rather than directly mapping the unlabeled samples to the classes.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
