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            <namePart type="given">Giorgio</namePart>
            <namePart type="given">Maria</namePart>
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    <abstract>This paper presents Conplext 1.0, a multilingual dataset designed for lexical complexity prediction in the context of second language (L2) learning. The resource covers 3,901 sentence contexts for 1,000 vocabulary items across five languages (English, French, Spanish, Swedish, and Dutch), each aligned with Common European Framework of Reference (CEFR) proficiency levels. Contexts were generated using a generative large language model and subsequently filtered for pedagogical suitability. A large-scale best–worst scaling (BWS) annotation experiment is being conducted with L2 learners to derive continuous, learner-informed lexical complexity values. The resulting dataset enables the development of context-aware word difficulty models that account for variation across both languages and learning stages. In addition to its primary use in lexical complexity prediction, Conplext provides valuable opportunities for research in word sense disambiguation, generative model evaluation, and adaptive language learning applications. By integrating computational and educational perspectives, this work advances the study of lexical difficulty in multilingual language learning environments.</abstract>
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%0 Conference Proceedings
%T Conplext 1.0: A Multilingual Lexical Complexity Prediction Dataset for L2 Learning
%A Alfter, David
%A Degraeuwe, Jasper
%Y Di Nunzio, Giorgio Maria
%Y Vezzani, Federica
%Y Ermakova, Liana
%Y Azarbonyad, Hosein
%Y Kamps, Jaap
%S Proceedings of the 2nd Workshop on Evaluating Text Difficulty in a Multilingual Context (DeTermIt! 2026)
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma, Mallorca (Spain)
%F alfter-degraeuwe-2026-conplext
%X This paper presents Conplext 1.0, a multilingual dataset designed for lexical complexity prediction in the context of second language (L2) learning. The resource covers 3,901 sentence contexts for 1,000 vocabulary items across five languages (English, French, Spanish, Swedish, and Dutch), each aligned with Common European Framework of Reference (CEFR) proficiency levels. Contexts were generated using a generative large language model and subsequently filtered for pedagogical suitability. A large-scale best–worst scaling (BWS) annotation experiment is being conducted with L2 learners to derive continuous, learner-informed lexical complexity values. The resulting dataset enables the development of context-aware word difficulty models that account for variation across both languages and learning stages. In addition to its primary use in lexical complexity prediction, Conplext provides valuable opportunities for research in word sense disambiguation, generative model evaluation, and adaptive language learning applications. By integrating computational and educational perspectives, this work advances the study of lexical difficulty in multilingual language learning environments.
%R 10.63317/4wbqzig7hgaw
%U https://aclanthology.org/2026.determit-1.3/
%U https://doi.org/10.63317/4wbqzig7hgaw
%P 22-32

Markdown (Informal)

[Conplext 1.0: A Multilingual Lexical Complexity Prediction Dataset for L2 Learning](https://aclanthology.org/2026.determit-1.3/) (Alfter & Degraeuwe, DeTermIt 2026)

• Conplext 1.0: A Multilingual Lexical Complexity Prediction Dataset for L2 Learning (Alfter & Degraeuwe, DeTermIt 2026)

ACL

• David Alfter and Jasper Degraeuwe. 2026. Conplext 1.0: A Multilingual Lexical Complexity Prediction Dataset for L2 Learning. In Proceedings of the 2nd Workshop on Evaluating Text Difficulty in a Multilingual Context (DeTermIt! 2026), pages 22–32, Palma, Mallorca (Spain). ELRA Language Resources Association (ELRA).