Reinforcement learning in natural language processing (NLP) became a hot topic of research in the artificial intelligence community no more than a year ago. Most of the research publications catering to the use of reinforcement learning in NLP were published in the latter half of 2017.
The biggest reason behind the use of a reinforcement learning framework in any domain is the representation of the environment in the form of state, an exhaustive list of all possible actions in the environment, and a domain-specific reward function to achieve the goal through the most optimized path of actions. Thus, if a system has many possible actions but the correct set of actions is not given, and the objective highly depends on different options (actions) of the system then reinforcement learning framework can model the system better than existing...
