GlotLID: Language Identification for Low-Resource Languages
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866916310051454976 |
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| author | Kargaran, Amir Hossein Imani, Ayyoob Yvon, François Schütze, Hinrich |
| author_facet | Kargaran, Amir Hossein Imani, Ayyoob Yvon, François Schütze, Hinrich |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_16248 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | GlotLID: Language Identification for Low-Resource Languages Kargaran, Amir Hossein Imani, Ayyoob Yvon, François Schütze, Hinrich Computation and Language 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. |
| title | GlotLID: Language Identification for Low-Resource Languages |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2310.16248 |