@inproceedings{andreas-etal-2017-translating,
title = "Translating Neuralese",
author = "Andreas, Jacob and
Dragan, Anca and
Klein, Dan",
editor = "Barzilay, Regina and
Kan, Min-Yen",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P17-1022/",
doi = "10.18653/v1/P17-1022",
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."
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%0 Conference Proceedings
%T Translating Neuralese
%A Andreas, Jacob
%A Dragan, Anca
%A Klein, Dan
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F andreas-etal-2017-translating
%X 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.
%R 10.18653/v1/P17-1022
%U https://aclanthology.org/P17-1022/
%U https://doi.org/10.18653/v1/P17-1022
%P 232-242
Markdown (Informal)
[Translating Neuralese](https://aclanthology.org/P17-1022/) (Andreas et al., ACL 2017)
ACL
- Jacob Andreas, Anca Dragan, and Dan Klein. 2017. Translating Neuralese. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 232–242, Vancouver, Canada. Association for Computational Linguistics.