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

TL;DR

This paper introduces OTEANN, an ANN-based method to measure the transparency of orthographies by evaluating phoneme-grapheme translation accuracy, providing insights into orthographic complexity and learner errors.

Contribution

It presents a novel neural network approach to quantify orthographic transparency, aligning with existing estimations and offering new insights into common learner mistakes.

Findings

Scores align with previous estimations of orthographic transparency.

Model reveals typical errors made by learners focusing solely on phonemic rules.

Applied to 17 orthographies, demonstrating broad applicability.

Abstract

To transcribe spoken language to written medium, most alphabets enable an unambiguous sound-to-letter rule. However, some writing systems have distanced themselves from this simple concept and little work exists in Natural Language Processing (NLP) on measuring such distance. In this study, we use an Artificial Neural Network (ANN) model to evaluate the transparency between written words and their pronunciation, hence its name Orthographic Transparency Estimation with an ANN (OTEANN). Based on datasets derived from Wikimedia dictionaries, we trained and tested this model to score the percentage of correct predictions in phoneme-to-grapheme and grapheme-to-phoneme translation tasks. The scores obtained on 17 orthographies were in line with the estimations of other studies. Interestingly, the model also provided insight into typical mistakes made by learners who only consider the phonemic rule in reading and writing.