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On Homophony and R'enyi Entropy

arXiv 2109.13766 ↗First posted 28 Sep 2021cs.CLcited by 2

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Abstract (arXiv)

Homophony's widespread presence in natural languages is a controversial topic. Recent theories of language optimality have tried to justify its prevalence, despite its negative effects on cognitive processing time; e.g., Piantadosi et al. (2012) argued homophony enables the reuse of efficient wordforms and is thus beneficial for languages. This hypothesis has recently been challenged by Trott and Bergen (2020), who posit that good wordforms are more often homophonous simply because they are more phonotactically probable. In this paper, we join in on the debate. We first propose a new information-theoretic quantification of a language's homophony: the sample R'enyi entropy. Then, we use this quantification to revisit Trott and Bergen's claims. While their point is theoretically sound, a specific methodological issue in their experiments raises doubts about their results. After addressing this issue, we find no clear pressure either towards or against homophony -- a much more nuanced result than either Piantadosi et al.'s or Trott and Bergen's findings.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 4 of its 44 references that are in the map. First posted on arXiv 28 Sep 2021.

Builds on

4 of 44 references.

References · 4

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  3. 2020Phonotactic Complexity and its Trade-offscited by 23
  4. 2019Meaning to Form: Measuring Systematicity as Informationcited by 16

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Citing papers per year: 2022: 1; 2023: 0; 2024: 0; 2025: 1.

2 papers.