论文标题
在生物医学文献中进行监督和半监督关系提取的对抗性学习
Adversarial Learning for Supervised and Semi-supervised Relation Extraction in Biomedical Literature
论文作者
论文摘要
对抗训练是一种通过在训练过程中涉及对抗性示例来改善模型性能的技术。在本文中，我们通过多个对抗性示例研究对抗训练，以使关系提取任务受益。我们还将对抗性训练技术应用于半监督场景中，以利用未标记的数据。蛋白质 - 蛋白质相互作用和蛋白质亚细胞定位任务的评估结果说明了对抗训练可改善监督模型，并且在半监督训练案例中也有效涉及未标记的数据。此外，我们的方法在两个基准数据集上实现了最先进的性能。
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.
