Spanish Dialect Classification: A Comparative Study of Linguistically Tailored Features, Unigrams and BERT Embeddings (2025.acl-srw)
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| Challenge: | Existing models for automatic dialect classification use bag-of-words unigram features instead of linguistic knowledge. |
| Approach: | They propose to use dialect-specific unigram features to train machine learning models . they also use a transformer-based model to find potentially useful dialect-related features . |
| Outcome: | The proposed model outperforms existing models but sacrifices explainability and interpretability. |
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| Challenge: | Multilingual pretrained language models have shown impressive results for cross-lingual transfer, but due to the constant model capacity, multilingual pre-training usually lags behind the monolingual competitors. |
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| Challenge: | linguistics do not characterize dialects as simple categories, but as collections of correlated features. |
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