PolyLingua: Margin-based Inter-class Transformer for Robust Cross-domain Language Detection

• URL: http://arxiv.org/abs/2512.08143v2
• Date: Wed, 10 Dec 2025 18:16:01 GMT
• Title: PolyLingua: Margin-based Inter-class Transformer for Robust Cross-domain Language Detection
• Abstract summary: PolyLingua is a lightweight Transformer-based model for in-domain language detection and fine-grained language classification.<n>It employs a two-level contrastive learning framework combining instance-level separation and class-level alignment with adaptive margins.<n>It achieves 99.25% F1 and 98.15% F1, respectively, surpassing Sonnet 3.5 while using 10x fewer parameters.
• Score: 6.237314351305131
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Language identification is a crucial first step in multilingual systems such as chatbots and virtual assistants, enabling linguistically and culturally accurate user experiences. Errors at this stage can cascade into downstream failures, setting a high bar for accuracy. Yet, existing language identification tools struggle with key cases -- such as music requests where the song title and user language differ. Open-source tools like LangDetect, FastText are fast but less accurate, while large language models, though effective, are often too costly for low-latency or low-resource settings. We introduce PolyLingua, a lightweight Transformer-based model for in-domain language detection and fine-grained language classification. It employs a two-level contrastive learning framework combining instance-level separation and class-level alignment with adaptive margins, yielding compact and well-separated embeddings even for closely related languages. Evaluated on two challenging datasets -- Amazon Massive (multilingual digital assistant utterances) and a Song dataset (music requests with frequent code-switching) -- PolyLingua achieves 99.25% F1 and 98.15% F1, respectively, surpassing Sonnet 3.5 while using 10x fewer parameters, making it ideal for compute- and latency-constrained environments.

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