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Dialogue research tends to distinguish between chit-chat and goal-orientedtasks. While the former is arguably more naturalistic and has a wider use oflanguage, the latter has clearer metrics and a straightforward learning signal.Humans effortlessly combine the two, for example engaging in chit-chat with thegoal of exchanging information or eliciting a specific response. Here, webridge the divide between these two domains in the setting of a richmulti-player text-based fantasy environment where agents and humans engage inboth actions and dialogue. Specifically, we train a goal-oriented model withreinforcement learning against an imitation-learned ``chit-chat'' model withtwo approaches: the policy either learns to pick a topic or learns to pick anutterance given the top-K utterances from the chit-chat model. We show thatboth models outperform an inverse model baseline and can converse naturallywith their dialogue partner in order to achieve goals.

I love your chain mail! Making knights smile in a fantasy game world: Open-domain goal-oriented dialogue agents