Publications
For news about publications, follow us on X: 
Click on any author names or tags to filter publications.
All topic tags:
surveydeep-rlmulti-agent-rlagent-modellingad-hoc-teamworkautonomous-drivinggoal-recognitionexplainable-aicausalgeneralisationsecurityemergent-communicationiterated-learningintrinsic-rewardsimulatorstate-estimationdeep-learningtransfer-learning
Selected tags (click to remove):
Simon-Kirby
2022
Shangmin Guo, Yi Ren, Kory Mathewson, Simon Kirby, Stefano V. Albrecht, Kenny Smith
Expressivity of Emergent Languages is a Trade-off between Contextual Complexity and Unpredictability
International Conference on Learning Representations, 2022
Abstract | BibTex | arXiv | Code
ICLRmulti-agent-rlemergent-communication
Abstract:
Researchers are using deep learning models to explore the emergence of language in various language games, where simulated agents interact and develop an emergent language to solve a task. We focus on the factors which determine the expressivity of emergent languages, which reflects the amount of information about input spaces those languages are capable of encoding. We measure the expressivity of emergent languages based on their generalisation performance across different games, and demonstrate that the expressivity of emergent languages is a trade-off between the complexity and unpredictability of the context those languages are used in. Another novel contribution of this work is the discovery of message type collapse. We also show that using the contrastive loss proposed by Chen et al. (2020) can alleviate this problem, compared with the standard referential loss used by the existing works.
@inproceedings{guo2022expressivity,
title={Expressivity of Emergent Languages is a Trade-off between Contextual Complexity and Unpredictability},
author={Shangmin Guo and Yi Ren and Kory Mathewson and Simon Kirby and Stefano V. Albrecht and Kenny Smith},
booktitle={International Conference on Learning Representations (ICLR)},
year={2022}
}
2021
Shangmin Guo, Yi Ren, Kory Mathewson, Simon Kirby, Stefano V. Albrecht, Kenny Smith
Expressivity of Emergent Language is a Trade-off between Contextual Complexity and Unpredictability
arXiv:2106.03982, 2021
Abstract | BibTex | arXiv
multi-agent-rlemergent-communication
Abstract:
Researchers are now using deep learning models to explore the emergence of language in various language games, where simulated agents interact and develop an emergent language to solve a task. Although it is quite intuitive that different types of language games posing different communicative challenges might require emergent languages which encode different levels of information, there is no existing work exploring the expressivity of the emergent languages. In this work, we propose a definition of partial order between expressivity based on the generalisation performance across different language games. We also validate the hypothesis that expressivity of emergent languages is a trade-off between the complexity and unpredictability of the context those languages are used in. Our second novel contribution is introducing contrastive loss into the implementation of referential games. We show that using our contrastive loss alleviates the collapse of message types seen using standard referential loss functions.
@misc{guo2021expressivity,
title={Expressivity of Emergent Language is a Trade-off between Contextual Complexity and Unpredictability},
author={Shangmin Guo and Yi Ren and Kory Mathewson and Simon Kirby and Stefano V. Albrecht and Kenny Smith},
year={2021},
eprint={2106.03982},
archivePrefix={arXiv},
primaryClass={cs.CL}
}