  Lifelong sequence generation (LSG), a problem in continual learning, aims to
continually train a model on a sequence of generation tasks to learn constantly
emerging new generation patterns while avoiding the forgetting of previous
knowledge. Existing LSG methods mainly focus on maintaining old knowledge while
paying little attention to knowledge transfer across tasks. In contrast, humans
can better learn new tasks by leveraging previously acquired knowledge from
similar tasks. Inspired by the learning paradigm of humans, we propose Dynamic
Module Expansion and Adaptation (DMEA), which enables the model to dynamically
determine the architecture for acquiring new knowledge based on task
correlation and select the most similar previous tasks to facilitate adaptation
to new tasks. In addition, as the learning process can easily be biased towards
the current task which might cause more severe forgetting of previously learned
knowledge, we propose dynamic gradient scaling to balance the learning of the
current task and replayed tasks. With extensive experiments, we demonstrate
that DMEA can consistently outperform existing methods in different LSG
settings.
