  Text-based Question Answering (QA) is a challenging task which aims at
finding short concrete answers for users' questions. This line of research has
been widely studied with information retrieval techniques and has received
increasing attention in recent years by considering deep neural network
approaches. Deep learning approaches, which are the main focus of this paper,
provide a powerful technique to learn multiple layers of representations and
interaction between questions and texts. In this paper, we provide a
comprehensive overview of different models proposed for the QA task, including
both traditional information retrieval perspective, and more recent deep neural
network perspective. We also introduce well-known datasets for the task and
present available results from the literature to have a comparison between
different techniques.
