Paying attention to the words: explaining readability prediction for French as a foreign language
(2024) 3rd Workshop on Tools and Resources for People with REAding DIfficulties (READI) — Location: Torino (20.May.2024)
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- Authors
Souza Wilkens, Rodrigo 
- Abstract
- Automatic text Readability Assessment (ARA) has been seen as a way of helping people with reading difficulties. Recent advancements in Natural Language Processing have shifted ARA from linguistic-based models to more precise black-box models. However, this shift has weakened the alignment between ARA models and the reading literature, potentially leading to inaccurate predictions based on unintended factors. In this paper, we investigate the explainability of ARA models, inspecting the relationship between attention mechanism scores, ARA features, and CEFR level predictions made by the model. We propose a method for identifying features associated with the predictions made by a model through the use of the attention mechanism. Exploring three feature families (i.e., psycho-linguistic, work frequency and graded lexicon), we associated features with the model’s attention heads. Finally, while not fully explanatory of the model’s performance, the correlations of these associations surpass those between features and text readability levels.
- Affiliations
Citations
Souza Wilkens, R., Watrin, P., & François, T. (2024). Paying attention to the words: explaining readability prediction for French as a foreign language. In Rodrigo Wilkens, Rémi Cardon, Amalia Todirascu and Núria Gala (ed.), Proceedings of 3rd Workshop on Tools and Resources for People with REAding DIfficulties (READI) (p. p. 102-115). ELRA Language Resource Association. https://hdl.handle.net/2078.5/231520