Improving Relation Extraction by Mitigating Over-Dependency on Entities through Adversarial Training

Current relation extraction models excessively rely on entities, making them vulnerable to adversarial attacks and limiting their generalization. An adversarial training method is proposed to address this issue by introducing both sequence- and token-level perturbations and a probabilistic strategy to encourage the model to leverage relational patterns in the context.