Natural Language Does Not Emerge `Naturally' in Multi-Agent Dialog

EMNLP 2017 Satwik KotturJos{\'e} MouraStefan LeeDhruv Batra

A number of recent works have proposed techniques for end-to-end learning of communication protocols among cooperative multi-agent populations, and have simultaneously found the emergence of grounded human-interpretable language in the protocols developed by the agents, learned without any human supervision! In this paper, using a Task {\&} Talk reference game between two agents as a testbed, we present a sequence of {`}negative{'} results culminating in a {`}positive{'} one {--} showing that while most agent-invented languages are effective (i.e. achieve near-perfect task rewards), they are decidedly not interpretable or compositional... (read more)

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