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. |
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| Challenge: | Existing approaches to few-shot named entity recognition (NER) focus on coarse-grained entities with few examples, while most unseen entities are fine-grounded. |
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| Challenge: | Existing methods to perform few-shot named entity recognition are limited and overfitting is caused by the spurious correlation resulting from the bias in selecting a few samples. |
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| Challenge: | Few-shot named entity recognition methods struggle with out-of-domain (OOD) examples due to their reliance on manual labeling for the target domain. |
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| Challenge: | Named entity recognition systems aim at recognizing unseen entity types based on a few labeled examples. |
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| Challenge: | Named-entity recognition (NER) requires large annotated datasets, which limits its applicability across domains with varying entity definitions. |
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Few-Shot Named Entity Recognition: An Empirical Baseline Study (2021.emnlp-main)
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Jiaxin Huang, Chunyuan Li, Krishan Subudhi, Damien Jose, Shobana Balakrishnan, Weizhu Chen, Baolin Peng, Jianfeng Gao, Jiawei Han
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