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

• URL: http://arxiv.org/abs/2208.11464v3
• Date: Tue, 26 Dec 2023 03:48:59 GMT
• Title: FactMix: Using a Few Labeled In-domain Examples to Generalize to Cross-domain Named Entity Recognition
• Authors: Linyi Yang, Lifan Yuan, Leyang Cui, Wenyang Gao, Yue Zhang
• Abstract summary: 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.
• Score: 42.32824906747491
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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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