  Lifelong learning aims to accumulate knowledge and alleviate catastrophic
forgetting when learning tasks sequentially. However, existing lifelong
language learning methods only focus on the supervised learning setting.
Unlabeled data, which can be easily accessed in real-world scenarios, are
underexplored. In this paper, we explore a novel setting, semi-supervised
lifelong language learning (SSLL), where a model learns sequentially arriving
language tasks with both labeled and unlabeled data. We propose an unlabeled
data enhanced lifelong learner to explore SSLL. Specially, we dedicate
task-specific modules to alleviate catastrophic forgetting and design two
modules to exploit unlabeled data: (1) a virtual supervision enhanced task
solver is constructed on a teacher-student framework to mine the underlying
knowledge from unlabeled data; and (2) a backward augmented learner is built to
encourage knowledge transfer from newly arrived unlabeled data to previous
tasks. Experimental results on various language tasks demonstrate our model's
effectiveness and superiority over competitive baselines under the new setting
SSLL.
