Assessing Readability by Filling Cloze Items with Transformers [Konferenzbericht] Paper presented at the International Conference on Artificial Intelligence in Education (22nd, Durham, UK, Jul 27-31, 2022).

Autor/in	Olney, Andrew M.
Titel	Assessing Readability by Filling Cloze Items with Transformers [Konferenzbericht] Paper presented at the International Conference on Artificial Intelligence in Education (22nd, Durham, UK, Jul 27-31, 2022).
Quelle	(2022), (13 Seiten) PDF als Volltext (1); PDF als Volltext kostenfreie Datei (2) Verfügbarkeit
Zusatzinformation	ORCID (Olney, Andrew M.) Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Readability; Cloze Procedure; Scores; Prediction; Models; Readability Formulas; Correlation; Outcome Measures; Flesch Reading Ease Formula + Suchen Sie Ihr Suchwort? Lesbarkeit; Lückentext; Vorhersage; Analogiemodell; Korrelation
Abstract	Cloze items are a foundational approach to assessing readability. However, they require human data collection, thus making them impractical in automated metrics. The present study revisits the idea of assessing readability with cloze items and compares human cloze scores and readability judgments with predictions made by T5, a popular deep learning architecture, on three corpora. Across all corpora, T5 predictions significantly correlated with human cloze scores and readability judgments, and in predictive models, they could be used interchangeably with average word length, a common readability predictor. For two corpora, combining T5 and Flesch reading ease predictors improved model fit for human cloze scores and readability judgments. (As Provided).
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01