@InProceedings{andreas-dragan-klein:2017:Long,
  author    = {Andreas, Jacob  and  Dragan, Anca  and  Klein, Dan},
  title     = {Translating Neuralese},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {232--242},
  abstract  = {Several approaches have recently been proposed for learning decentralized deep
	multiagent policies that coordinate via a differentiable communication channel.
	While these policies are effective for many tasks, interpretation of their
	induced communication strategies has remained a challenge. Here we propose to
	interpret agents' messages by translating them.  Unlike in typical machine
	translation problems, we have no parallel data to learn from. Instead we
	develop
	a translation model based on the insight that agent messages and natural
	language strings mean the same thing if they induce the same belief about
	the world in a listener.  We present theoretical guarantees and empirical
	evidence that our approach preserves both the semantics and pragmatics of
	messages by ensuring that players communicating through a translation layer do
	not suffer a substantial loss in reward relative to players with a common
	language.},
  url       = {http://aclweb.org/anthology/P17-1022}
}