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Compositionality Through Language Transmission, using Artificial Neural Networks

arXiv 2101.11739 ↗First posted 27 Jan 2021cs.CLcited by 0

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Abstract (arXiv)

We propose an architecture and process for using the Iterated Learning Model ("ILM") for artificial neural networks. We show that ILM does not lead to the same clear compositionality as observed using DCGs, but does lead to a modest improvement in compositionality, as measured by holdout accuracy and topologic similarity. We show that ILM can lead to an anti-correlation between holdout accuracy and topologic rho. We demonstrate that ILM can increase compositionality when using non-symbolic high-dimensional images as input.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 17 of its 21 references that are in the map. First posted on arXiv 27 Jan 2021.

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References · 17

  1. 2015Distilling the Knowledge in a Neural Networkcited by 10,076
  2. 2018Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representationscited by 929
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  10. 2019Measuring Compositionality in Representation Learningcited by 84
  11. 2020Compositional Languages Emerge in a Neural Iterated Learning Modelcited by 75
  12. 2019On the Pitfalls of Measuring Emergent Communicationcited by 66
5 more
  1. 2019Ease-of-Teaching and Language Structure from Emergent Communicationcited by 65
  2. 2018How agents see things: On visual representations in an emergent language gamecited by 45
  3. 2020Co-evolution of language and agents in referential gamescited by 18
  4. 2019Emergence of Compositional Language with Deep Generational Transmissioncited by 17
  5. 2017Translating Neuralesecited by 16

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