Papers by Daniel Grießhaber
Fine-tuning BERT for Low-Resource Natural Language Understanding via Active Learning (2020.coling-main)
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| Challenge: | Recent work has explored the suitability of pre-trained language models in low resource settings with less than 1,000 training data points. |
| Approach: | They propose to use pool-based active learning to speed up training while keeping the cost of labeling new data constant. |
| Outcome: | The proposed model can be fine-tuned to optimize for low-resource settings while keeping the cost of labeling constant. |