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        <title>Compression-Based Linguistic Complexity Metrics in Automatic Essay Scoring</title>
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    <abstract>Compression-based linguistic complexity metrics enable cross-linguistic comparison without prior annotation. Their sensitivity to variation across languages and Portuguese registers highlights their applicability in NLP tasks. This study investigates their use as readability proxies and complementary features in Automatic Essay Scoring. We analyze how these metrics capture variation in essay quality across traits, genres, and educational levels in Brazilian Portuguese. In addition, we evaluate their sensitivity to differences between humanand AI-generated essays. Our results suggest that complexity metrics are effective (i) in differentiating educational levels, (ii) in detecting whether they were written by humans and (iii) as predictors of essay quality.</abstract>
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%0 Conference Proceedings
%T Compression-Based Linguistic Complexity Metrics in Automatic Essay Scoring
%A Serras, Felipe Ribas
%A Silveira, Igor Cataneo
%A Mauá, Denis Deratani
%A Finger, Marcelo
%Y Barbosa, Bryan Khelven da Silva
%Y Paes, Aline
%Y Felippo, Ariani Di
%S Proceedings of the 17th Brazilian Symposium in Information and Human Language Technology
%D 2026
%8 October
%I Association for Computational Linguistics
%C Cuiabá, Mato Grosso, Brazil
%F serras-etal-2026-compression
%X Compression-based linguistic complexity metrics enable cross-linguistic comparison without prior annotation. Their sensitivity to variation across languages and Portuguese registers highlights their applicability in NLP tasks. This study investigates their use as readability proxies and complementary features in Automatic Essay Scoring. We analyze how these metrics capture variation in essay quality across traits, genres, and educational levels in Brazilian Portuguese. In addition, we evaluate their sensitivity to differences between humanand AI-generated essays. Our results suggest that complexity metrics are effective (i) in differentiating educational levels, (ii) in detecting whether they were written by humans and (iii) as predictors of essay quality.
%R 10.5753/stil.2026.26572
%U https://aclanthology.org/2026.stil-1.32/
%U https://doi.org/10.5753/stil.2026.26572
%P 390-404

Markdown (Informal)

[Compression-Based Linguistic Complexity Metrics in Automatic Essay Scoring](https://aclanthology.org/2026.stil-1.32/) (Serras et al., STIL 2026)

• Compression-Based Linguistic Complexity Metrics in Automatic Essay Scoring (Serras et al., STIL 2026)

ACL

• Felipe Ribas Serras, Igor Cataneo Silveira, Denis Deratani Mauá, and Marcelo Finger. 2026. Compression-Based Linguistic Complexity Metrics in Automatic Essay Scoring. In Proceedings of the 17th Brazilian Symposium in Information and Human Language Technology, pages 390–404, Cuiabá, Mato Grosso, Brazil. Association for Computational Linguistics.