Publication

Co-evolution of language and agents in referential games

Conference of the European Chapter of the Association for Computational Linguistics (EACL)


Abstract

Referential games offer a grounded learning environment for neural agents which accounts for the fact that language is functionally used to communicate. However, they do not take into account a second constraint considered to be fundamental for the shape of human language: that it must be learnable by new language learners.

Cogswell et al. (2019) introduced cultural transmission within referential games through a changing population of agents to constrain the emerging language to be learnable. However, the resulting languages remain inherently biased by the agents’ underlying capabilities.

In this work, we introduce Language Transmission Simulator to model both cultural and architectural evolution in a population of agents. As our core contribution, we empirically show that the optimal situation is to take into account also the learning biases of the language learners and thus let language and agents coevolve. When we allow the agent population to evolve through architectural evolution, we achieve across the board improvements on all considered metrics and surpass the gains made with cultural transmission. These results stress the importance of studying the underlying agent architecture and pave the way to investigate the co-evolution of language and agent in language emergence studies.

Related Publications

All Publications

SIGGRAPH - August 9, 2021

Control Strategies for Physically Simulated Characters Performing Two-player Competitive Sports

Jungdam Won, Deepak Gopinath, Jessica Hodgins

CVPR - June 20, 2021

Ego-Exo: Transferring Visual Representations from Third-person to First-person Videos

Yanghao Li, Tushar Nagarajan, Bo Xiong, Kristen Grauman

ICML - July 18, 2021

Align, then memorise: the dynamics of learning with feedback alignment

Maria Refinetti, Stéphane d'Ascoli, Ruben Ohana, Sebastian Goldt

CVPR - June 19, 2021

Intentonomy: a Dataset and Study towards Human Intent Understanding

Menglin Jia, Zuxuan Wu, Austin Reiter, Claire Cardie, Serge Belongie, Ser-Nam Lim

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookies Policy