We show that in addition to using past dialogue, these models are able to effectively use the state of the underlying world to condition their predictions.
we show that grounding on the details of the local environment, including location descriptions, and the objects (and their affordances) and characters (and their previous actions) present within it allows better predictions of agent behavior and dialogue.