Papers with SEE-Few
SEE-Few: Seed, Expand and Entail for Few-shot Named Entity Recognition (2022.coling-1)
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| Challenge: | Existing few-shot named entity recognition methods focus on leveraging existing datasets in the rich-resource domains which might fail in training-from-scratch setting. |
| Approach: | They propose a multi-task learning framework for Few-shot named entity recognition without using source domain data. |
| Outcome: | The proposed framework outperforms state-of-the-art few-shot named entity recognition methods on a training-from-scratch dataset. |