Papers with active learning baselines
Active Learning with Non-Uniform Costs for African Natural Language Processing (2026.findings-eacl)
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| Challenge: | Annotating datasets for African languages is challenging due to the continent's vast linguistic diversity, complicating development of NLP systems. |
| Approach: | They propose a cost-aware active learning method that integrates BatchBALD acquisition strategy with a 0-1 Knapsack optimization objective to select informative and budget-efficient samples. |
| Outcome: | The proposed method outperforms BALD, BatchBALD, and stochastic sampling variants across cost scenarios on the MasakhaNEWS multilingual news classification benchmark covering 11 African languages. |