OTEANN: Estimating the Transparency of Orthographies with an Artificial Neural Network
This work addresses a gap in NLP for quantifying orthographic transparency, which is incremental as it applies an existing method to new data.
The paper tackled the problem of measuring the transparency between written words and pronunciation in orthographies using an artificial neural network (OTEANN), achieving scores on 17 orthographies that aligned with existing studies and providing insights into learner mistakes.
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.