@inproceedings{boldt-mortensen-2025-searching,
title = "Searching for the Most Human-like Emergent Language",
author = "Boldt, Brendon and
Mortensen, David R.",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.1188/",
pages = "23300--23318",
ISBN = "979-8-89176-332-6",
abstract = "In this paper, we design a signalling game-based emergent communication environment to generate state-of-the-art emergent languages in terms of similarity to human language. This is done with hyperparameter optimization, using XferBench as the objective function. XferBench quantifies the statistical similarity of emergent language to human language by measuring its suitability for deep transfer learning to human language. Additionally, we demonstrate the predictive power of entropy on the transfer learning performance of emergent language as well as corroborate previous results on the entropy-minimization properties of emergent communication systems. Finally, we report generalizations regarding what hyperparameters produce more realistic emergent languages, that is, ones which transfer better to human language."
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%0 Conference Proceedings
%T Searching for the Most Human-like Emergent Language
%A Boldt, Brendon
%A Mortensen, David R.
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F boldt-mortensen-2025-searching
%X In this paper, we design a signalling game-based emergent communication environment to generate state-of-the-art emergent languages in terms of similarity to human language. This is done with hyperparameter optimization, using XferBench as the objective function. XferBench quantifies the statistical similarity of emergent language to human language by measuring its suitability for deep transfer learning to human language. Additionally, we demonstrate the predictive power of entropy on the transfer learning performance of emergent language as well as corroborate previous results on the entropy-minimization properties of emergent communication systems. Finally, we report generalizations regarding what hyperparameters produce more realistic emergent languages, that is, ones which transfer better to human language.
%U https://aclanthology.org/2025.emnlp-main.1188/
%P 23300-23318
Markdown (Informal)
[Searching for the Most Human-like Emergent Language](https://aclanthology.org/2025.emnlp-main.1188/) (Boldt & Mortensen, EMNLP 2025)
ACL
- Brendon Boldt and David R. Mortensen. 2025. Searching for the Most Human-like Emergent Language. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 23300–23318, Suzhou, China. Association for Computational Linguistics.