  Persuasion games are fundamental in economics and AI research and serve as
the basis for important applications. However, work on this setup assumes
communication with stylized messages that do not consist of rich human
language. In this paper we consider a repeated sender (expert) -- receiver
(decision maker) game, where the sender is fully informed about the state of
the world and aims to persuade the receiver to accept a deal by sending one of
several possible natural language reviews. We design an automatic expert that
plays this repeated game, aiming to achieve the maximal payoff. Our expert is
implemented within the Monte Carlo Tree Search (MCTS) algorithm, with deep
learning models that exploit behavioral and linguistic signals in order to
predict the next action of the decision maker, and the future payoff of the
expert given the state of the game and a candidate review. We demonstrate the
superiority of our expert over strong baselines, its adaptability to different
decision makers, and that its selected reviews are nicely adapted to the
proposed deal.
