Named Entity Recognition (NER) in Few-Shot setting is imperative for entity tagging in low resource domains. Existing approaches only learn class-specific semantic features and intermediate representations from source domains. This affects generalizability to unseen target domains, resulting in suboptimal performances. To this end, we present CONTAINER, a novel contrastive learning technique that optimizes the inter-token distribution distance for Few-Shot NER. Instead of optimizing class-specific attributes, CONTAINER optimizes a generalized objective of differentiating between token categories based on their Gaussian-distributed embeddings. This effectively alleviates overfitting issues originating from training domains. Our experiments in several traditional test domains (OntoNotes, CoNLL'03, WNUT'17, GUM) and a new large scale Few-Shot NER dataset (Few-NERD) demonstrate that, on average, CONTAINER outperforms previous methods by 3%-13% absolute F1 points while showing consistent performance trends, even in challenging scenarios where previous approaches could not achieve appreciable performance. The source code of CONTAINER will be available at: https://github.com/psunlpgroup/CONTaiNER.
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CITATION STYLE
Snigdha, S., Das, S., Katiyar, A., Passonneau, R. J., & Zhang, R. (2022). CONTAINER: Few-Shot Named Entity Recognition via Contrastive Learning. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (Vol. 1, pp. 6338–6353). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2022.acl-long.439