Papers with OpenLID

2 papers
MaskLID: Code-Switching Language Identification through Iterative Masking (2024.acl-short)

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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.
Approach: They propose a simple yet effective code-switching language identification method that uses the LID itself to mask features associated with L1 and L2 in the next round.
Outcome: The proposed method is based on two open-source LIDs based in the FastText architecture and does not require any external resources.
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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