  Conversational agents have become ubiquitous, ranging from goal-oriented
systems for helping with reservations to chit-chat models found in modern
virtual assistants. In this survey paper, we explore this fascinating field. We
look at some of the pioneering work that defined the field and gradually move
to the current state-of-the-art models. We look at statistical, neural,
generative adversarial network based and reinforcement learning based
approaches and how they evolved. Along the way we discuss various challenges
that the field faces, lack of context in utterances, not having a good
quantitative metric to compare models, lack of trust in agents because they do
not have a consistent persona etc. We structure this paper in a way that
answers these pertinent questions and discusses competing approaches to solve
them.
