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.
Abstract (translated)
在这篇论文中，我们设计了一个基于信号博弈的新兴通信环境，以生成在与人类语言相似性方面处于行业前沿的新兴语言。这是通过超参数优化实现的，其中使用XferBench作为目标函数。XferBench 通过衡量新兴语言进行深度迁移学习到人类语言的适用性来量化其与人类语言的统计相似度。此外，我们展示了熵在新兴语言迁移学习性能中的预测能力，并且证实了关于新兴通信系统最小化熵特性的先前结果。最后，我们报告了哪些超参数可以生成更接近现实世界的新兴语言的一般结论，即那些更好地迁移到人类语言的语言。
URL
https://arxiv.org/abs/2510.03467
