  A suitable state representation is a fundamental part of the learning process
in Reinforcement Learning. In various tasks, the state can either be described
by natural language or be natural language itself. This survey outlines the
strategies used in the literature to build natural language state
representations. We appeal for more linguistically interpretable and grounded
representations, careful justification of design decisions and evaluation of
the effectiveness of different approaches.
