  In the era of deep learning, modeling for most NLP tasks has converged to
several mainstream paradigms. For example, we usually adopt the sequence
labeling paradigm to solve a bundle of tasks such as POS-tagging, NER,
Chunking, and adopt the classification paradigm to solve tasks like sentiment
analysis. With the rapid progress of pre-trained language models, recent years
have observed a rising trend of Paradigm Shift, which is solving one NLP task
by reformulating it as another one. Paradigm shift has achieved great success
on many tasks, becoming a promising way to improve model performance. Moreover,
some of these paradigms have shown great potential to unify a large number of
NLP tasks, making it possible to build a single model to handle diverse tasks.
In this paper, we review such phenomenon of paradigm shifts in recent years,
highlighting several paradigms that have the potential to solve different NLP
tasks.
