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FactMix: Using a Few Labeled In-domain Examples to Generalize to Cross-domain Named Entity Recognition

24 Aug 2022COLING 2022 10arXiv:2208.11464archive 2025-07-28

Linyi Yang, Lifan Yuan, Leyang Cui, Wenyang Gao, Yue Zhang

Few-shot Named Entity Recognition (NER) is imperative for entity tagging in limited resource domains and thus received proper attention in recent years. Existing approaches for few-shot NER are evaluated mainly under in-domain settings. In contrast, little is known about how these inherently faithful models perform in cross-domain NER using a few labeled in-domain examples. This paper proposes a two-step rationale-centric data augmentation method to improve the model's generalization ability. Results on several datasets show that our model-agnostic method significantly improves the performance of cross-domain NER tasks compared to previous state-of-the-art methods, including the data augmentation and prompt-tuning methods. Our codes are available at https://github.com/lifan-yuan/FactMix.

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collate_fn lifan-yuan/factmix/process_data.py official repository ran MIT (permissive) · 8d44e37e16020525 · report
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Tasks

Cross-Domain Named Entity RecognitionData AugmentationFew-shot NERNamed Entity RecognitionNamed Entity Recognition (NER)

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