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The Emergence of Compositional Languages for Numeric Concepts Through Iterated Learning in Neural Agents

arXiv 1910.05291 ↗First posted 11 Oct 2019cs.CL cs.AI cs.LGcited by 26

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Abstract (arXiv)

Since first introduced, computer simulation has been an increasingly important tool in evolutionary linguistics. Recently, with the development of deep learning techniques, research in grounded language learning has also started to focus on facilitating the emergence of compositional languages without pre-defined elementary linguistic knowledge. In this work, we explore the emergence of compositional languages for numeric concepts in multi-agent communication systems. We demonstrate that compositional language for encoding numeric concepts can emerge through iterated learning in populations of deep neural network agents. However, language properties greatly depend on the input representations given to agents. We found that compositional languages only emerge if they require less iterations to be fully learnt than other non-degenerate languages for agents on a given input representation.

Key facts. Cited by 26 papers in the map (data through 14 Oct 2026). First posted on arXiv 11 Oct 2019.

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Citing papers per year: 2021: 3; 2022: 5; 2023: 5; 2024: 7; 2025: 3; 2026: 3.

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  1. 2022Emergent Communication for Understanding Human Language Evolution: What's Missing?cited by 23
  2. 2021Iterated learning for emergent systematicity in VQAcited by 21
  3. 2021Multi-label Iterated Learning for Image Classification with Label Ambiguitycited by 19
  4. 2022Linking Emergent and Natural Languages via Corpus Transfercited by 19
  5. 2024Iterated Learning Improves Compositionality in Large Vision-Language Modelscited by 17
  6. 2024Bias Amplification in Language Model Evolution: An Iterated Learning Perspectivecited by 17
  7. 2024A Review of the Applications of Deep Learning-Based Emergent Communicationcited by 17
  8. 2023Towards More Human-like AI Communication: A Review of Emergent Communication Researchcited by 13
  9. 2021Expressivity of Emergent Language is a Trade-off between Contextual Complexity and Unpredictabilitycited by 11
  10. 2022Emergent Quantized Communicationcited by 7
  11. 2024The Curious Case of Representational Alignment: Unravelling Visio-Linguistic Tasks in Emergent Communicationcited by 7
  12. 2024lpNTK: Better Generalisation with Less Data via Sample Interaction During Learningcited by 6
14 more
  1. 2023Visual Referential Games Further the Emergence of Disentangled Representationscited by 5
  2. 2023Learning Multi-Object Positional Relationships via Emergent Communicationcited by 4
  3. 2025Unsupervised Translation of Emergent Communicationcited by 3
  4. 2022Meta-Referential Games to Learn Compositional Learning Behaviourscited by 2
  5. 2024ELCC: the Emergent Language Corpus Collectioncited by 2
  6. 2025Implicit Repair with Reinforcement Learning in Emergent Communicationcited by 2
  7. 2023Discrete Messages Improve Communication Efficiency among Isolated Intelligent Agentscited by 1
  8. 2024Learning Efficient Recursive Numeral Systems via Reinforcement Learningcited by 1
  9. 2026Learning to Communicate Across Modalities: Perceptual Heterogeneity in Multi-Agent Systemscited by 1
  10. 2022Using Representation Expressiveness and Learnability to Evaluate Self-Supervised Learning Methodscited by 0
  11. 2023ETHER: Aligning Emergent Communication for Hindsight Experience Replaycited by 0
  12. 2025Cognitively-Inspired Emergent Communication via Knowledge Graphs for Assisting the Visually Impairedcited by 0
  13. 2026Evaluating the relationship between regularity and learnability in recursive numeral systems using Reinforcement Learningcited by 0
  14. 2026Math Takes Two: A test for emergent mathematical reasoning in communicationcited by 0