Searching for the Most Human-like Emergent Language
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 12 of its 39 references that are in the map. First posted on arXiv 3 Oct 2025.
Builds on
12 of 39 references.
References · 12
- 2016Categorical Reparameterization with Gumbel-Softmaxcited by 2,708
- 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,455
- 2014On the Properties of Neural Machine Translation: Encoder-Decoder Approachescited by 1,303
- 2016The Concrete Distribution: A Continuous Relaxation of Discrete Random Variablescited by 1,135
- 2020Emergent Multi-Agent Communication in the Deep Learning Eracited by 168
- 2023Tree-Structured Parzen Estimator: Understanding Its Algorithm Components and Their Roles for Better Empirical Performancecited by 143
- 2019LSTM Networks Can Perform Dynamic Countingcited by 45
- 2019EGG: a toolkit for research on Emergence of lanGuage in Gamescited by 42
- 2021Emergent Communication of Generalizationscited by 41
- 2022Linking Emergent and Natural Languages via Corpus Transfercited by 19
- 2024A Review of the Applications of Deep Learning-Based Emergent Communicationcited by 17
- 2024ELCC: the Emergent Language Corpus Collectioncited by 2
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Citing papers per year: 2026: 1.
1 paper.