  Current natural language processing models work well on a single task, yet
they often fail to continuously learn new tasks without forgetting previous
ones as they are re-trained throughout their lifetime, a challenge known as
lifelong learning. State-of-the-art lifelong language learning methods store
past examples in episodic memory and replay them at both training and inference
time. However, as we show later in our experiments, there are three significant
impediments: (1) needing unrealistically large memory module to achieve good
performance, (2) suffering from negative transfer, (3) requiring multiple local
adaptation steps for each test example that significantly slows down the
inference speed. In this paper, we identify three common principles of lifelong
learning methods and propose an efficient meta-lifelong framework that combines
them in a synergistic fashion. To achieve sample efficiency, our method trains
the model in a manner that it learns a better initialization for local
adaptation. Extensive experiments on text classification and question answering
benchmarks demonstrate the effectiveness of our framework by achieving
state-of-the-art performance using merely 1% memory size and narrowing the gap
with multi-task learning. We further show that our method alleviates both
catastrophic forgetting and negative transfer at the same time.
