@inproceedings{katinskaia-etal-2025-estimation,
title = "Estimation of Text Difficulty in the Context of Language Learning",
author = "Katinskaia, Anisia and
Vu, Anh-Duc and
Hou, Jue and
Vanhatalo, Ulla and
Wu, Yiheng and
Yangarber, Roman",
editor = {Kochmar, Ekaterina and
Alhafni, Bashar and
Bexte, Marie and
Burstein, Jill and
Horbach, Andrea and
Laarmann-Quante, Ronja and
Tack, Ana{\"i}s and
Yaneva, Victoria and
Yuan, Zheng},
booktitle = "Proceedings of the 20th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2025)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.bea-1.43/",
doi = "10.18653/v1/2025.bea-1.43",
pages = "594--611",
ISBN = "979-8-89176-270-1",
abstract = "Easy language and text simplification are currently topical research questions, with important applications in many contexts, and with various approaches under active investigation, including prompt-based methods. The estimation of the level of difficulty of a text becomes a crucial challenge when the estimator is employed in a simplification workflow as a quality-control mechanism. It can act as a \textit{critic} in frameworks where it can guide other models, which are responsible for generating text at a specified level of difficulty, as determined by the user{'}s needs.We present our work in the context of simplified Finnish. We discuss problems in collecting corpora for training models for estimation of text difficulty, and our experiments with estimation models.The results of the experiments are promising: the models appear usable both for assessment and for deployment as a component in a larger simplification framework."
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<abstract>Easy language and text simplification are currently topical research questions, with important applications in many contexts, and with various approaches under active investigation, including prompt-based methods. The estimation of the level of difficulty of a text becomes a crucial challenge when the estimator is employed in a simplification workflow as a quality-control mechanism. It can act as a critic in frameworks where it can guide other models, which are responsible for generating text at a specified level of difficulty, as determined by the user’s needs.We present our work in the context of simplified Finnish. We discuss problems in collecting corpora for training models for estimation of text difficulty, and our experiments with estimation models.The results of the experiments are promising: the models appear usable both for assessment and for deployment as a component in a larger simplification framework.</abstract>
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%0 Conference Proceedings
%T Estimation of Text Difficulty in the Context of Language Learning
%A Katinskaia, Anisia
%A Vu, Anh-Duc
%A Hou, Jue
%A Vanhatalo, Ulla
%A Wu, Yiheng
%A Yangarber, Roman
%Y Kochmar, Ekaterina
%Y Alhafni, Bashar
%Y Bexte, Marie
%Y Burstein, Jill
%Y Horbach, Andrea
%Y Laarmann-Quante, Ronja
%Y Tack, Anaïs
%Y Yaneva, Victoria
%Y Yuan, Zheng
%S Proceedings of the 20th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-270-1
%F katinskaia-etal-2025-estimation
%X Easy language and text simplification are currently topical research questions, with important applications in many contexts, and with various approaches under active investigation, including prompt-based methods. The estimation of the level of difficulty of a text becomes a crucial challenge when the estimator is employed in a simplification workflow as a quality-control mechanism. It can act as a critic in frameworks where it can guide other models, which are responsible for generating text at a specified level of difficulty, as determined by the user’s needs.We present our work in the context of simplified Finnish. We discuss problems in collecting corpora for training models for estimation of text difficulty, and our experiments with estimation models.The results of the experiments are promising: the models appear usable both for assessment and for deployment as a component in a larger simplification framework.
%R 10.18653/v1/2025.bea-1.43
%U https://aclanthology.org/2025.bea-1.43/
%U https://doi.org/10.18653/v1/2025.bea-1.43
%P 594-611
Markdown (Informal)
[Estimation of Text Difficulty in the Context of Language Learning](https://aclanthology.org/2025.bea-1.43/) (Katinskaia et al., BEA 2025)
ACL
- Anisia Katinskaia, Anh-Duc Vu, Jue Hou, Ulla Vanhatalo, Yiheng Wu, and Roman Yangarber. 2025. Estimation of Text Difficulty in the Context of Language Learning. In Proceedings of the 20th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2025), pages 594–611, Vienna, Austria. Association for Computational Linguistics.