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arXiv · 2002.02878

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

Shrimai Prabhumoye·Margaret Li·Jack Urbanek·Emily Dinan·Douwe Kiela·Jason Weston·Arthur Szlam
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Abstract

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

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Shrimai Prabhumoye, Margaret Li, Jack Urbanek, Emily Dinan, Douwe Kiela, Jason Weston, Arthur Szlam. 2020-02-07. I love your chain mail! Making knights smile in a fantasy game world: Open-domain goal-oriented dialogue agents. https://arxiv.org/abs/2002.02878

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