论文标题
COVID-19信息服务的无监督域适应与对抗性域混合
Unsupervised Domain Adaptation for COVID-19 Information Service with Contrastive Adversarial Domain Mixup
论文作者
论文摘要
在COVID-19错误信息检测的现实应用中，一个基本挑战是缺乏标记的COVID数据来实现对模型的端到端培训，尤其是在大流行的早期阶段。为了应对这一挑战，我们使用对比度学习和对抗性域混合提出了一个无监督的域适应框架，以将知识从现有源数据域转移到目标covid-19数据域。特别是，为了弥合源域和目标域之间的差距，我们的方法降低了这两个域之间基于径向基函数（RBF）的差异。此外，我们利用域对抗性示例的力量来建立中间域混合，其中在训练过程中可以混合来自两个域的输入文本的潜在表示。在多个现实世界数据集上进行的广泛实验表明，与最先进的基线相比，我们的方法可以有效地将错误信息检测系统适应未见的COVID-19目标域，并有显着改进。
In the real-world application of COVID-19 misinformation detection, a fundamental challenge is the lack of the labeled COVID data to enable supervised end-to-end training of the models, especially at the early stage of the pandemic. To address this challenge, we propose an unsupervised domain adaptation framework using contrastive learning and adversarial domain mixup to transfer the knowledge from an existing source data domain to the target COVID-19 data domain. In particular, to bridge the gap between the source domain and the target domain, our method reduces a radial basis function (RBF) based discrepancy between these two domains. Moreover, we leverage the power of domain adversarial examples to establish an intermediate domain mixup, where the latent representations of the input text from both domains could be mixed during the training process. Extensive experiments on multiple real-world datasets suggest that our method can effectively adapt misinformation detection systems to the unseen COVID-19 target domain with significant improvements compared to the state-of-the-art baselines.
