  We propose a novel task, G4C, to study teacher-student natural language
interactions in a goal-driven and grounded environment. Dungeons and Dragons
(D&D), a role-playing game, provides an ideal setting to investigate such
interactions. Here, the Dungeon Master (DM), i.e., the teacher, guides the
actions of several players -- students, each with their own personas and
abilities -- to achieve shared goals grounded in a fantasy world. Our approach
is to decompose and model these interactions into (1) the DM's intent to guide
players toward a given goal; (2) the DM's guidance utterance to the players
expressing this intent; and (3) a theory-of-mind (ToM) model that anticipates
the players' reaction to the guidance one turn into the future. We develop a
novel reinforcement learning (RL) method for training a DM that generates
guidance for players by rewarding utterances where the intent matches the
ToM-anticipated player actions. Human and automated evaluations show that a DM
trained to explicitly model intents and incorporate ToM of the players using RL
generates better-quality guidance that is 3x more likely to fulfill the DM's
intent than a vanilla natural language generation (NLG) approach.
