FABRA: French Aggregator-Based Readability Assessment toolkit

Rodrigo Wilkens, David Alfter, Xiaoou Wang, Alice Pintard, Anaïs Tack, Kevin P. Yancey, Thomas François

Abstract
In this paper, we present the FABRA: readability toolkit based on the aggregation of a large number of readability predictor variables. The toolkit is implemented as a service-oriented architecture, which obviates the need for installation, and simplifies its integration into other projects. We also perform a set of experiments to show which features are most predictive on two different corpora, and how the use of aggregators improves performance over standard feature-based readability prediction. Our experiments show that, for the explored corpora, the most important predictors for native texts are measures of lexical diversity, dependency counts and text coherence, while the most important predictors for foreign texts are syntactic variables illustrating language development, as well as features linked to lexical sophistication. FABRA: have the potential to support new research on readability assessment for French.
Anthology ID:
2022.lrec-1.130
Volume:
Proceedings of the Thirteenth Language Resources and Evaluation Conference
Month:
June
Year:
2022
Address:
Marseille, France
Editors:
Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
1217–1233
Language:
URL:
https://aclanthology.org/2022.lrec-1.130
DOI:
Bibkey:
Cite (ACL):
Rodrigo Wilkens, David Alfter, Xiaoou Wang, Alice Pintard, Anaïs Tack, Kevin P. Yancey, and Thomas François. 2022. FABRA: French Aggregator-Based Readability Assessment toolkit. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 1217–1233, Marseille, France. European Language Resources Association.
Cite (Informal):
FABRA: French Aggregator-Based Readability Assessment toolkit (Wilkens et al., LREC 2022)
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https://preview.aclanthology.org/nschneid-patch-1/2022.lrec-1.130.pdf
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