OTEANN: Estimating the Transparency of Orthographies with an Artificial Neural Network · HackerPapers discussions

OTEANN: Estimating the Transparency of Orthographies with an Artificial Neural Network — mentioned by int_19h

Mon, 07 Jul 2025 20:45:48 GMT

English is singled out because its orthography really is atrociously bad even when compared with other languages. https://arxiv.org/pdf/1912.13321

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OTEANN: Estimating the Transparency of Orthographies with an Artificial Neural Network — mentioned by int_19h

Mon, 07 Jul 2025 20:08:12 GMT

The complexity of these rules, and the number of exceptions that you need to learn notwithstanding the rules, can be roughly estimated for any given language by training a language model on word <-> IPA correspondence for that language (using a subset of the vocabulary as a training set), and then seeing how well it can predict the remaining words. You can run it in either direction, too, to separately measure the difficulty of reading (word -> IPA) and writing (IPA -> word) that language. This was actually done for a number of languages including English: https://arxiv.org/abs/1912.13321 You…

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OTEANN: Estimating the Transparency of Orthographies with an Artificial Neural Network — mentioned by dadoum

Mon, 07 Jul 2025 13:16:20 GMT

According to this paper [0] and my own experience, it's way easier to pronounce a word in French given the spelled word than in English. It's slightly harder to spell French than English for the model of the study, but it's really close. Now, in my personal experience, I feel like French has a lot of rules while English has a lot of outliers which do not follow any rules. But my native language is French, so I am obviously biased. [0]: https://arxiv.org/pdf/1912.13321

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OTEANN: Estimating the Transparency of Orthographies with an Artificial Neural Network — mentioned by int_19h

Wed, 03 May 2023 02:30:44 GMT

There was an amusing study where they tried to measure how phonemic various language orthographies are by training an ANN model on some data to convert back and forth between spelling and pronunciation, and then checking the error rate on more inputs: https://arxiv.org/abs/1912.13321. Here's the table from that paper; numbers are percentages of accurate guesses. Orthography Write Read ----------- ---------- ---------- ar 84.3 ± 0.8 99.4 ± 0.3 br 80.6 ± 0.6 77.2 ± 1.6 de 69.1 ± 1.0 78.0 ± 1.5 en 36.1 ± 1.5 31.1 ± 1.3 eo 99.3 ± 0.2 99.7 ± 0.1 es 66.9 ± 2.0 85.3 ± 1.3 fi 97.7 ± 0.3 92.3 ± 0.8 fr…

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OTEANN: Estimating the Transparency of Orthographies with an Artificial Neural Network — mentioned by gok

Tue, 03 Nov 2020 19:09:32 GMT

This is more generally the concept of Orthographic depth [1] A recent study attempted to categorize languages and found English and French to be among the most opaque, Finnish and Korean to be the most transparent, and German in the middle. [2] [1] https://en.wikipedia.org/wiki/Orthographic_depth [2] https://arxiv.org/abs/1912.13321

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