OpenLID-v3: Improving the Precision of Closely Related Language Identification -- An Experience Report
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Computer Science > Computation and Language
Title:OpenLID-v3: Improving the Precision of Closely Related Language Identification -- An Experience Report
Abstract:Language identification (LID) is an essential step in building high-quality multilingual datasets from web data. Existing LID tools (such as OpenLID or GlotLID) often struggle to identify closely related languages and to distinguish valid natural language from noise, which contaminates language-specific subsets, especially for low-resource languages. In this work we extend the OpenLID classifier by adding more training data, merging problematic language variant clusters, and introducing a special label for marking noise. We call this extended system OpenLID-v3 and evaluate it against GlotLID on multiple benchmarks. During development, we focus on three groups of closely related languages (Bosnian, Croatian, and Serbian; Romance varieties of Northern Italy and Southern France; and Scandinavian languages) and contribute new evaluation datasets where existing ones are inadequate. We find that ensemble approaches improve precision but also substantially reduce coverage for low-resource languages. OpenLID-v3 is available on this https URL.
| Comments: | VarDial'26 workshop at the EACL 2026 conference |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2602.13139 [cs.CL] |
| (or arXiv:2602.13139v4 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2602.13139
arXiv-issued DOI via DataCite
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| Related DOI: | https://doi.org/10.18653/v1/2026.vardial-1.23
DOI(s) linking to related resources
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Submission history
From: Mariia Fedorova [view email][v1] Fri, 13 Feb 2026 17:47:08 UTC (63 KB)
[v2] Mon, 23 Feb 2026 17:38:41 UTC (69 KB)
[v3] Tue, 16 Jun 2026 11:13:12 UTC (70 KB)
[v4] Thu, 18 Jun 2026 11:51:21 UTC (70 KB)
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