Continuous Detection, Rapidly React: Unseen Rumors Detection based on Continual Prompt-Tuning
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Since open social platforms allow for a large and continuous flow of unverified information, rumors can emerge unexpectedly and spread quickly. However, existing rumor detection (RD) models often assume the same training and testing distributions and cannot cope with the continuously changing social network environment. This paper proposes a Continual Prompt-Tuning RD (CPT-RD) framework, which avoids catastrophic forgetting of upstream tasks during sequential task learning and enables knowledge transfer between domain tasks. To avoid forgetting, we optimize and store task-special soft-prompt for each domain. Furthermore, we also propose several strategies to transfer knowledge of upstream tasks to deal with emergencies and a task-conditioned prompt-wise hypernetwork (TPHNet) to consolidate past domains, enabling bidirectional knowledge transfer. Finally, CPT-RD is evaluated on English and Chinese RD datasets and is effective and efficient compared to state-of-the-art baselines, without data replay techniques and with only a few parameter tuning.
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