GlotLID: Language Identification for Low-Resource Languages

• URL: http://arxiv.org/abs/2310.16248v3
• Date: Tue, 2 Jul 2024 23:34:35 GMT
• Title: GlotLID: Language Identification for Low-Resource Languages
• Authors: Amir Hossein Kargaran, Ayyoob Imani, François Yvon, Hinrich Schütze,
• Abstract summary: GlotLID-M is 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.
• Score: 51.38634652914054
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 (including future versions), code, and list of data sources are available: https://github.com/cisnlp/GlotLID.

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