  We study sequential language games in which two players, each with private
information, communicate to achieve a common goal. In such games, a successful
player must (i) infer the partner's private information from the partner's
messages, (ii) generate messages that are most likely to help with the goal,
and (iii) reason pragmatically about the partner's strategy. We propose a model
that captures all three characteristics and demonstrate their importance in
capturing human behavior on a new goal-oriented dataset we collected using
crowdsourcing.
