{
  "id": "1706.08502",
  "version": "v1",
  "published": "2017-06-26T17:47:46.000Z",
  "updated": "2017-06-26T17:47:46.000Z",
  "title": "Natural Language Does Not Emerge 'Naturally' in Multi-Agent Dialog",
  "authors": [
    "Satwik Kottur",
    "José M. F. Moura",
    "Stefan Lee",
    "Dhruv Batra"
  ],
  "comment": "9 pages, 7 figures, 2 tables",
  "categories": [
    "cs.CL",
    "cs.AI",
    "cs.CV"
  ],
  "abstract": "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, all learned without any human supervision! In this paper, using a Task and Tell 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. In essence, we find that natural language does not emerge 'naturally', despite the semblance of ease of natural-language-emergence that one may gather from recent literature. We discuss how it is possible to coax the invented languages to become more and more human-like and compositional by increasing restrictions on how two agents may communicate.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2017-06-26T17:47:46.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "natural language",
      "multi-agent dialog",
      "tell reference game",
      "communication protocols",
      "cooperative multi-agent populations"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 9,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}