Multilingual text classification
14 papers with code • 0 benchmarks • 2 datasets
Benchmarks
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Most implemented papers
Easy Adaptation to Mitigate Gender Bias in Multilingual Text Classification
Existing approaches to mitigate demographic biases evaluate on monolingual data, however, multilingual data has not been examined.
SLICER: Sliced Fine-Tuning for Low-Resource Cross-Lingual Transfer for Named Entity Recognition
Large multilingual language models generally demonstrate impressive results in zero-shot cross-lingual transfer, yet often fail to successfully transfer to low-resource languages, even for token-level prediction tasks like named entity recognition (NER).
Exploring Multilingual Text Data Distillation
In the paper, we propose several data distillation techniques for multilingual text classification datasets using language-model-based learning methods.
Comparison between parameter-efficient techniques and full fine-tuning: A case study on multilingual news article classification
Adapters and Low-Rank Adaptation (LoRA) are parameter-efficient fine-tuning techniques designed to make the training of language models more efficient.