CommonLID: New Benchmark Reveals Language Identification Models Overestimate Accuracy on Web Data
Researchers introduced CommonLID, a human-annotated language identification benchmark covering 109 languages, with emphasis on previously under-served languages. The benchmark tests eight popular language identification models and reveals that existing evaluations significantly overestimate accuracy for many languages when applied to noisy web data. This finding is important for improving multilingual text corpus curation, a foundational step in training multilingual language models.
A large collaborative research team has released CommonLID, a community-driven benchmark dataset for evaluating language identification (LID) models on web domain data across 109 languages. The benchmark addresses a critical gap in NLP: while language identification is essential for building high-quality multilingual corpora, existing evaluation datasets do not adequately represent the noisy, heterogeneous nature of real web data. By testing eight popular LID models against CommonLID alongside five other common evaluation sets, the researchers demonstrated that current models perform worse on web data than previous benchmarks suggested. The study highlights that many languages—particularly those historically under-represented in NLP research—have been inadequately evaluated. The researchers have released both CommonLID and the code used to create it under an open license, providing the community with a more realistic evaluation framework for developing language identification systems.
What different sources said
- arXiv cs.CLCenter
CommonLID: Re-evaluating State-of-the-Art Language Identification Performance on Web Data
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