GlotLID: Language Identification for Low-Resource Languages (2023.findings-emnlp)
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| Challenge: | Existing web-mined datasets for low-resource languages have been useful for low resource NLP. |
| Approach: | They propose a model that identifies 1665 low-resource languages and a new model that is rigorously evaluated and reliable. |
| Outcome: | The proposed model outperforms baselines when balancing F1 and false positive rate (FPR). |
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| Challenge: | Existing LID systems perform poorly on low-resource languages, causing 'representation washing', where the community is given a false view of the actual progress of low-source NLP. |
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| Challenge: | Low-resource languages and dialects remain difficult to identify and categorize accurately due to data in these languages and are limited to single-domain data. |
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| Challenge: | Sentence-level LIDs are classifiers trained on monolingual texts to provide single labels, typically using a softmax layer to turn scores into probabilities. |
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| Challenge: | Pretrained large language models (LLMs) can bridge the performance gap for under-resourced languages by substantial margins, as measured by both automatic and human evaluations. |
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Abteen Ebrahimi, Arya D. McCarthy, Arturo Oncevay, John E. Ortega, Luis Chiruzzo, Gustavo Giménez-Lugo, Rolando Coto-Solano, Katharina Kann
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| Challenge: | Existing studies have examined the quality of labeled data in non-English languages. |
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