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

I Cast Detect Thoughts: Learning to Converse and Guide with Intents and Theory-of-Mind in Dungeons and Dragons