ConLID: Supervised Contrastive Learning for Low-Resource Language Identification

• URL: http://arxiv.org/abs/2506.15304v1
• Date: Wed, 18 Jun 2025 09:35:33 GMT
• Title: ConLID: Supervised Contrastive Learning for Low-Resource Language Identification
• Authors: Negar Foroutan, Jakhongir Saydaliev, Ye Eun Kim, Antoine Bosselut,
• Abstract summary: We propose a novel supervised contrastive learning (SCL) approach to learn domain-invariant representations for low-resource languages.<n>We show that our approach improves LID performance on out-of-domain data for low-resource languages by 3.2%.
• Score: 14.504528263331075
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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. Through an extensive analysis, we show that our approach improves LID performance on out-of-domain data for low-resource languages by 3.2%, demonstrating its effectiveness in enhancing LID models.

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