  QA models with lifelong learning (LL) abilities are important for practical
QA applications, and architecture-based LL methods are reported to be an
effective implementation for these models. However, it is non-trivial to extend
previous approaches to QA tasks since they either require access to task
identities in the testing phase or do not explicitly model samples from unseen
tasks. In this paper, we propose Diana: a dynamic architecture-based lifelong
QA model that tries to learn a sequence of QA tasks with a prompt enhanced
language model. Four types of hierarchically organized prompts are used in
Diana to capture QA knowledge from different granularities. Specifically, we
dedicate task-level prompts to capture task-specific knowledge to retain high
LL performances and maintain instance-level prompts to learn knowledge shared
across different input samples to improve the model's generalization
performance. Moreover, we dedicate separate prompts to explicitly model unseen
tasks and introduce a set of prompt key vectors to facilitate knowledge sharing
between tasks. Extensive experiments demonstrate that Diana outperforms
state-of-the-art lifelong QA models, especially in handling unseen tasks.
