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        <title>Assessing sentence readability for German language learners with broad linguistic modeling or readability formulas: When do linguistic insights make a difference?</title>
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    <abstract>We present a new state-of-the-art sentence-wise readability assessment model for German L2 readers. We build a linguistically broadly informed machine learning model and compare its performance against four commonly used readability formulas. To understand when the linguistic insights used to inform our model make a difference for readability assessment and when simple readability formulas suffice, we compare their performance based on two common automatic readability assessment tasks: predictive regression and sentence pair ranking. We find that leveraging linguistic insights yields top performances across tasks, but that for the identification of simplified sentences also readability formulas – which are easier to compute and more accessible – can be sufficiently precise. Linguistically informed modeling, however, is the only viable option for high quality outcomes in fine-grained prediction tasks. We then explore the sentence-wise readability profile of leveled texts written for language learners at a beginning, intermediate, and advanced level of German to showcase the valuable insights that sentence-wise readability assessment can have for the adaptation of learning materials and better understand how sentences’ individual readability contributes to larger texts’ overall readability.</abstract>
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%0 Conference Proceedings
%T Assessing sentence readability for German language learners with broad linguistic modeling or readability formulas: When do linguistic insights make a difference?
%A Weiss, Zarah
%A Meurers, Detmar
%S Proceedings of the 17th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2022)
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, Washington
%F weiss-meurers-2022-assessing
%X We present a new state-of-the-art sentence-wise readability assessment model for German L2 readers. We build a linguistically broadly informed machine learning model and compare its performance against four commonly used readability formulas. To understand when the linguistic insights used to inform our model make a difference for readability assessment and when simple readability formulas suffice, we compare their performance based on two common automatic readability assessment tasks: predictive regression and sentence pair ranking. We find that leveraging linguistic insights yields top performances across tasks, but that for the identification of simplified sentences also readability formulas – which are easier to compute and more accessible – can be sufficiently precise. Linguistically informed modeling, however, is the only viable option for high quality outcomes in fine-grained prediction tasks. We then explore the sentence-wise readability profile of leveled texts written for language learners at a beginning, intermediate, and advanced level of German to showcase the valuable insights that sentence-wise readability assessment can have for the adaptation of learning materials and better understand how sentences’ individual readability contributes to larger texts’ overall readability.
%R 10.18653/v1/2022.bea-1.19
%U https://aclanthology.org/2022.bea-1.19
%U https://doi.org/10.18653/v1/2022.bea-1.19
%P 141-153

Markdown (Informal)

[Assessing sentence readability for German language learners with broad linguistic modeling or readability formulas: When do linguistic insights make a difference?](https://aclanthology.org/2022.bea-1.19) (Weiss & Meurers, BEA 2022)

• Assessing sentence readability for German language learners with broad linguistic modeling or readability formulas: When do linguistic insights make a difference? (Weiss & Meurers, BEA 2022)

ACL

• Zarah Weiss and Detmar Meurers. 2022. Assessing sentence readability for German language learners with broad linguistic modeling or readability formulas: When do linguistic insights make a difference?. In Proceedings of the 17th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2022), pages 141–153, Seattle, Washington. Association for Computational Linguistics.