  While reinforcement learning (RL) has been successful in natural language
processing (NLP) domains such as dialogue generation and text-based games, it
typically faces the problem of sparse rewards that leads to slow or no
convergence. Traditional methods that use text descriptions to extract only a
state representation ignore the feedback inherently present in them. In
text-based games, for example, descriptions like "Good Job! You ate the food}"
indicate progress, and descriptions like "You entered a new room" indicate
exploration. Positive and negative cues like these can be converted to rewards
through sentiment analysis. This technique converts the sparse reward problem
into a dense one, which is easier to solve. Furthermore, this can enable
reinforcement learning without rewards, in which the agent learns entirely from
these intrinsic sentiment rewards. This framework is similar to intrinsic
motivation, where the environment does not necessarily provide the rewards, but
the agent analyzes and realizes them by itself. We find that providing dense
rewards in text-based games using sentiment analysis improves performance under
some conditions.
