GlotLID: Language Identification for Low-Resource Languages
Abstract
Domain fit: AI-adjacent · Paper appears method- or tooling-adjacent to AI workflows with partial ecosystem coverage.
Several recent papers have published good solutions for language identification (LID) for about 300 high-resource and medium-resource languages.However, there is no LID available that (i) covers a wide range of low-resource languages, (ii) is rigorously evaluated and reliable and (iii) efficient and easy to use.Here, we publish GlotLID-M, an LID model that satisfies the desiderata of wide coverage, reliability and efficiency.It identifies 1665 languages, a large increase in coverage compared to prior work.In our experiments, GlotLID-M outperforms four baselines (CLD3, FT176, OpenLID and NLLB) when balancing F1 and false positive rate (FPR).We analyze the unique challenges that low-resource LID poses: incorrect corpus metadata, leakage from high-resource languages, difficulty separating closely related languages, handling of macrolanguage vs varieties and in general noisy data.We hope that integrating GlotLID-M into dataset creation pipelines will improve quality and enhance accessibility of NLP technology for low-resource languages and cultures.GlotLID-M model, code, and list of data sources are available: https: //github.com/cisnlp/GlotLID.
Results and benchmarks
Several recent papers have published good solutions for language identification (LID) for about 300 high-resource and medium-resource languages.However, there is no LID available that (i) covers a wide range of low-resource languages, (ii) is rigorously evaluated and reliable and (iii) efficient and easy to use.Here, we publish GlotLID-M, an LID model that satisfies the desiderata of wide coverage, reliability and efficiency.It identifies 1665 languages, a large increase in coverage compared to prior work.In our experiments, GlotLID-M outperforms four baselines (CLD3, FT176, OpenLID and NLLB) when balancing F1 and false positive rate (FPR).We analyze the unique challenges that low-resource LID poses: incorrect corpus metadata, leakage from high-resource languages, difficulty separating closely related languages, handling of macrolanguage vs varieties and in general noisy data.We hope that integrating GlotLID-M into dataset creation pipelines will improve quality and enhance accessibility of NLP technology for low-resource languages and cultures.GlotLID-M model, code, and list of data sources are available: https: //github.com/cisnlp/GlotLID.
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Evidence graph: 2 refs, 1 links.
Utility signals: depth 65/100, grounding 58/100, status medium.
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Research context
15
Citations
0
References
Tasks
Computer science, Metadata, Publication, Resource (disambiguation), Identification (biology), Reliability (semiconductor), World Wide Web
Methods
Information retrieval
Domains
Natural language processing, Artificial intelligence
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