  Effectively scaling large Transformer models is a main driver of recent
advances in natural language processing. Dynamic neural networks, as an
emerging research direction, are capable of scaling up neural networks with
sub-linear increases in computation and time by dynamically adjusting their
computational path based on the input. Dynamic neural networks could be a
promising solution to the growing parameter numbers of pretrained language
models, allowing both model pretraining with trillions of parameters and faster
inference on mobile devices. In this survey, we summarize progress of three
types of dynamic neural networks in NLP: skimming, mixture of experts, and
early exit. We also highlight current challenges in dynamic neural networks and
directions for future research.
