  As a main field of artificial intelligence, natural language processing (NLP)
has achieved remarkable success via deep neural networks. Plenty of NLP tasks
have been addressed in a unified manner, with various tasks being associated
with each other through sharing the same paradigm. However, neural networks are
black boxes and rely on probability computation. Making mistakes is inevitable.
Therefore, estimating the reliability and trustworthiness (in other words,
uncertainty) of neural networks becomes a key research direction, which plays a
crucial role in reducing models' risks and making better decisions. Therefore,
in this survey, we provide a comprehensive review of uncertainty-relevant works
in the NLP field. Considering the data and paradigms characteristics, we first
categorize the sources of uncertainty in natural language into three types,
including input, system, and output. Then, we systemically review uncertainty
quantification approaches and the main applications. Finally, we discuss the
challenges of uncertainty estimation in NLP and discuss potential future
directions, taking into account recent trends in the field. Though there have
been a few surveys about uncertainty estimation, our work is the first to
review uncertainty from the NLP perspective.
