Question Answering has recently received high attention from artificial
intelligence communities due to the advancements in learning technologies.
Early question answering models used rule-based approaches and moved to the
statistical approach to address the vastly available information. However,
statistical approaches are shown to underperform in handling the dynamic nature
and the variation of language. Therefore, learning models have shown the
capability of handling the dynamic nature and variations in language. Many deep
learning methods have been introduced to question answering. Most of the deep
learning approaches have shown to achieve higher results compared to machine
learning and statistical methods. The dynamic nature of language has profited
from the nonlinear learning in deep learning. This has created prominent
success and a spike in work on question answering. This paper discusses the
successes and challenges in question answering question answering systems and
techniques that are used in these challenges