Adversarial Learning for Supervised and Semi-supervised Relation Extraction in Biomedical Literature
This work addresses relation extraction problems for biomedical researchers, offering incremental improvements through adversarial training techniques.
The paper tackles relation extraction in biomedical literature by applying adversarial training with multiple adversarial examples to supervised models and extending it to semi-supervised scenarios to leverage unlabeled data. The method achieves state-of-the-art performance on protein-protein interaction and protein subcellular localization tasks, showing improvements in both supervised and semi-supervised cases.
Adversarial training is a technique of improving model performance by involving adversarial examples in the training process. In this paper, we investigate adversarial training with multiple adversarial examples to benefit the relation extraction task. We also apply adversarial training technique in semi-supervised scenarios to utilize unlabeled data. The evaluation results on protein-protein interaction and protein subcellular localization task illustrate adversarial training provides improvement on the supervised model, and is also effective on involving unlabeled data in the semi-supervised training case. In addition, our method achieves state-of-the-art performance on two benchmarking datasets.