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

• URL: http://arxiv.org/abs/2002.02878v2
• Date: Mon, 10 Feb 2020 20:45:20 GMT
• Title: I love your chain mail! Making knights smile in a fantasy game world: Open-domain goal-oriented dialogue agents
• Authors: Shrimai Prabhumoye and Margaret Li and Jack Urbanek and Emily Dinan and Douwe Kiela and Jason Weston and Arthur Szlam
• Abstract summary: We train a goal-oriented model with reinforcement learning against an imitation-learned 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.
• Score: 69.68400056148336
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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