  Continual learning systems will interact with humans, with each other, and
with the physical world through time -- and continue to learn and adapt as they
do. An important open problem for continual learning is a large-scale benchmark
that enables realistic evaluation of algorithms. In this paper, we study a
natural setting for continual learning on a massive scale. We introduce the
problem of personalized online language learning (POLL), which involves fitting
personalized language models to a population of users that evolves over time.
To facilitate research on POLL, we collect massive datasets of Twitter posts.
These datasets, Firehose10M and Firehose100M, comprise 100 million tweets,
posted by one million users over six years. Enabled by the Firehose datasets,
we present a rigorous evaluation of continual learning algorithms on an
unprecedented scale. Based on this analysis, we develop a simple algorithm for
continual gradient descent (ConGraD) that outperforms prior continual learning
methods on the Firehose datasets as well as earlier benchmarks. Collectively,
the POLL problem setting, the Firehose datasets, and the ConGraD algorithm
enable a complete benchmark for reproducible research on web-scale continual
learning.
