Clarifying orthography: Orthographic transparency as compressibility
Not on the map. Why?
Abstract (arXiv)
Orthographic transparency -- how directly spelling is related to sound -- lacks a unified, script-agnostic metric. Using ideas from algorithmic information theory, we quantify orthographic transparency in terms of the mutual compressibility between orthographic and phonological strings. Our measure provides a principled way to combine two factors that decrease orthographic transparency, capturing both irregular spellings and rule complexity in one quantity. We estimate our transparency measure using prequential code-lengths derived from neural sequence models. Evaluating 22 languages across a broad range of script types (alphabetic, abjad, abugida, syllabic, logographic) confirms common intuitions about relative transparency of scripts. Mutual compressibility offers a simple, principled, and general yardstick for orthographic transparency.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 2 of its 28 references that are in the map. First posted on arXiv 19 May 2025.
Builds on
2 of 28 references.
References · 2
- 2017Convolutional Sequence to Sequence Learningcited by 552
- 2018The Description Length of Deep Learning Modelscited by 72
Built on by
Citing papers per year: 2026: 1.
1 paper.