Hardness-guided domain adaptation to recognise biomedical named entities under low-resource scenarios (2022.emnlp-main)
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| Challenge: | Named Entity Recognition (NER) tasks require a large amount of training data and domains are often scarcely labeled. |
| Approach: | They propose a hardness-guided domain adaptation framework for bioNER tasks that leverages domain hardness information to improve the adaptability of the learnt model in low-resource scenarios. |
| Outcome: | The proposed model outperforms the state-of-the-art MetaNER model on biomedical datasets. |
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