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. |
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| Challenge: | Extensive evaluations of masking BERT, RoBERTa, and DistilBERT on eleven diverse NLP tasks show that our binary masked language models encode information necessary for solving downstream tasks. |
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Code-Switched Language Identification is Harder Than You Think (2024.eacl-long)
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| Challenge: | Code switching (CS) is a common phenomenon in written and spoken communication, but is handled poorly by many NLP applications. |
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An Open Dataset and Model for Language Identification (2023.acl-short)
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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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