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        <title>ConLID: Supervised Contrastive Learning for Low-Resource Language Identification</title>
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    <name type="personal">
        <namePart type="given">Antoine</namePart>
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            <namePart type="given">Vera</namePart>
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    <abstract>Language identification (LID) is a critical step in curating multilingual LLM pretraining corpora from web crawls. While many studies on LID model training focus on collecting diverse training data to improve performance, low-resource languages – often limited to single-domain data, such as the Bible – continue to perform poorly. To resolve these class imbalance and bias issues, we propose a novel supervised contrastive learning (SCL) approach to learn domain-invariant representations for low-resource languages. We show that our approach improves LID performance on out-of-domain data for low-resource languages by 3.2 percentage points, while maintaining its performance for the high-resource languages.</abstract>
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%0 Conference Proceedings
%T ConLID: Supervised Contrastive Learning for Low-Resource Language Identification
%A Foroutan, Negar
%A Saydaliev, Jakhongir
%A Kim, Grace
%A Bosselut, Antoine
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-380-7
%F foroutan-etal-2026-conlid
%X Language identification (LID) is a critical step in curating multilingual LLM pretraining corpora from web crawls. While many studies on LID model training focus on collecting diverse training data to improve performance, low-resource languages – often limited to single-domain data, such as the Bible – continue to perform poorly. To resolve these class imbalance and bias issues, we propose a novel supervised contrastive learning (SCL) approach to learn domain-invariant representations for low-resource languages. We show that our approach improves LID performance on out-of-domain data for low-resource languages by 3.2 percentage points, while maintaining its performance for the high-resource languages.
%U https://aclanthology.org/2026.eacl-long.315/
%P 6693-6708

Markdown (Informal)

[ConLID: Supervised Contrastive Learning for Low-Resource Language Identification](https://aclanthology.org/2026.eacl-long.315/) (Foroutan et al., EACL 2026)

• ConLID: Supervised Contrastive Learning for Low-Resource Language Identification (Foroutan et al., EACL 2026)

ACL

• Negar Foroutan, Jakhongir Saydaliev, Grace Kim, and Antoine Bosselut. 2026. ConLID: Supervised Contrastive Learning for Low-Resource Language Identification. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 6693–6708, Rabat, Morocco. Association for Computational Linguistics.