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        <title>Experiments with Universal CEFR Classification</title>
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        <namePart type="given">Sowmya</namePart>
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            <title>Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications</title>
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        <name type="personal">
            <namePart type="given">Joel</namePart>
            <namePart type="family">Tetreault</namePart>
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            <namePart type="given">Ekaterina</namePart>
            <namePart type="family">Kochmar</namePart>
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    <abstract>The Common European Framework of Reference (CEFR) guidelines describe language proficiency of learners on a scale of 6 levels. While the description of CEFR guidelines is generic across languages, the development of automated proficiency classification systems for different languages follow different approaches. In this paper, we explore universal CEFR classification using domain-specific and domain-agnostic, theory-guided as well as data-driven features. We report the results of our preliminary experiments in monolingual, cross-lingual, and multilingual classification with three languages: German, Czech, and Italian. Our results show that both monolingual and multilingual models achieve similar performance, and cross-lingual classification yields lower, but comparable results to monolingual classification.</abstract>
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    <identifier type="doi">10.18653/v1/W18-0515</identifier>
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%0 Conference Proceedings
%T Experiments with Universal CEFR Classification
%A Vajjala, Sowmya
%A Rama, Taraka
%Y Tetreault, Joel
%Y Burstein, Jill
%Y Kochmar, Ekaterina
%Y Leacock, Claudia
%Y Yannakoudakis, Helen
%S Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F vajjala-rama-2018-experiments
%X The Common European Framework of Reference (CEFR) guidelines describe language proficiency of learners on a scale of 6 levels. While the description of CEFR guidelines is generic across languages, the development of automated proficiency classification systems for different languages follow different approaches. In this paper, we explore universal CEFR classification using domain-specific and domain-agnostic, theory-guided as well as data-driven features. We report the results of our preliminary experiments in monolingual, cross-lingual, and multilingual classification with three languages: German, Czech, and Italian. Our results show that both monolingual and multilingual models achieve similar performance, and cross-lingual classification yields lower, but comparable results to monolingual classification.
%R 10.18653/v1/W18-0515
%U https://aclanthology.org/W18-0515
%U https://doi.org/10.18653/v1/W18-0515
%P 147-153

Markdown (Informal)

[Experiments with Universal CEFR Classification](https://aclanthology.org/W18-0515) (Vajjala & Rama, BEA 2018)

• Experiments with Universal CEFR Classification (Vajjala & Rama, BEA 2018)

ACL

• Sowmya Vajjala and Taraka Rama. 2018. Experiments with Universal CEFR Classification. In Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications, pages 147–153, New Orleans, Louisiana. Association for Computational Linguistics.