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Ease-of-Teaching and Language Structure from Emergent Communication

arXiv 1906.02403 ↗First posted 6 Jun 2019cs.AI cs.CL cs.LGcited by 65

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Abstract (arXiv)

Artificial agents have been shown to learn to communicate when needed to complete a cooperative task. Some level of language structure (e.g., compositionality) has been found in the learned communication protocols. This observed structure is often the result of specific environmental pressures during training. By introducing new agents periodically to replace old ones, sequentially and within a population, we explore such a new pressure -- ease of teaching -- and show its impact on the structure of the resulting language.

Key facts. Cited by 65 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Jun 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 14; 2022: 14; 2023: 14; 2024: 14; 2025: 3; 2026: 6.

65 papers.

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  1. 2022What Artificial Neural Networks Can Tell Us About Human Language Acquisitioncited by 97
  2. 2024Learning from Teaching Regularization: Generalizable Correlations Should be Easy to Imitatecited by 46
  3. 2021Emergent Communication of Generalizationscited by 41
  4. 2022Fortuitous Forgetting in Connectionist Networkscited by 39
  5. 2023Language Model Alignment with Elastic Resetcited by 34
  6. 2021Few-shot Language Coordination by Modeling Theory of Mindcited by 32
  7. 2022Emergent Communication: Generalization and Overfitting in Lewis Gamescited by 28
  8. 2022Emergent Communication for Understanding Human Language Evolution: What's Missing?cited by 23
  9. 2021Iterated learning for emergent systematicity in VQAcited by 21
  10. 2021Multi-label Iterated Learning for Image Classification with Label Ambiguitycited by 19
  11. 2022Linking Emergent and Natural Languages via Corpus Transfercited by 19
  12. 2024Iterated Learning Improves Compositionality in Large Vision-Language Modelscited by 17
38 more
  1. 2024A Review of the Applications of Deep Learning-Based Emergent Communicationcited by 17
  2. 2022Emergent Communication through Metropolis-Hastings Naming Game with Deep Generative Modelscited by 16
  3. 2021The Emergence of the Shape Bias Results from Communicative Efficiencycited by 13
  4. 2023What makes a language easy to deep-learn? Deep neural networks and humans similarly benefit from compositional structurecited by 13
  5. 2024A Complexity-Based Theory of Compositionalitycited by 13
  6. 2021Catalytic Role Of Noise And Necessity Of Inductive Biases In The Emergence Of Compositional Communicationcited by 12
  7. 2023Towards More Human-like AI Communication: A Review of Emergent Communication Researchcited by 12
  8. 2021Expressivity of Emergent Language is a Trade-off between Contextual Complexity and Unpredictabilitycited by 11
  9. 2022Distributed Reinforcement Learning for Robot Teams: A Reviewcited by 11
  10. 2023Lewis's Signaling Game as beta-VAE For Natural Word Lengths and Segmentscited by 11
  11. 2021Dynamic population-based meta-learning for multi-agent communication with natural languagecited by 10
  12. 2023Referential communication in heterogeneous communities of pre-trained visual deep networkscited by 10
  13. 2023EC^2: Emergent Communication for Embodied Controlcited by 10
  14. 2021Calibrate your listeners! Robust communication-based training for pragmatic speakerscited by 9
  15. 2022Defending Compositionality in Emergent Languagescited by 8
  16. 2024NeLLCom-X: A Comprehensive Neural-Agent Framework to Simulate Language Learning and Group Communicationcited by 8
  17. 2022Emergent Quantized Communicationcited by 7
  18. 2024Concept-Best-Matching: Evaluating Compositionality in Emergent Communicationcited by 7
  19. 2024The Curious Case of Representational Alignment: Unravelling Visio-Linguistic Tasks in Emergent Communicationcited by 7
  20. 2021The emergence of visual semantics through communication gamescited by 5
  21. 2023Visual Referential Games Further the Emergence of Disentangled Representationscited by 5
  22. 2024Language Evolution with Deep Learningcited by 5
  23. 2021Neural networks can understand compositional functions that humans do not, in the context of emergent communicationcited by 4
  24. 2022Over-communicate no more: Situated RL agents learn concise communication protocolscited by 4
  25. 2023Learning Multi-Object Positional Relationships via Emergent Communicationcited by 4
  26. 2026From Signals to Structure: How Memory Architecture Drives Language Emergence in LLM Agentscited by 4
  27. 2021TexRel: a Green Family of Datasets for Emergent Communications on Relationscited by 3
  28. 2022Symbol Emergence as Inter-personal Categorization with Head-to-head Latent Wordcited by 2
  29. 2022Meta-Referential Games to Learn Compositional Learning Behaviourscited by 2
  30. 2023Models of symbol emergence in communication: a conceptual review and a guide for avoiding local minimacited by 2
  31. 2023Emergent Communication with Attentioncited by 2
  32. 2024"Forgetting" in Machine Learning and Beyond: A Surveycited by 2
  33. 2024ELCC: the Emergent Language Corpus Collectioncited by 2
  34. 2024Semantics and Spatiality of Emergent Communicationcited by 2
  35. 2025Implicit Repair with Reinforcement Learning in Emergent Communicationcited by 2
  36. 2021gComm: An environment for investigating generalization in Grounded Language Acquisitioncited by 1
  37. 2022Contrastive Multimodal Learning for Emergence of Graphical Sensory-Motor Communicationcited by 1
  38. 2023On the Correspondence between Compositionality and Imitation in Emergent Neural Communicationcited by 1

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