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 compared to the counterfactual data augmentation and prompt-tuning methods.- Anthology ID:
- 2022.coling-1.476
- Volume:
- Proceedings of the 29th International Conference on Computational Linguistics
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editors:
- Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 5360–5371
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.476
- DOI:
- Cite (ACL):
- Linyi Yang, Lifan Yuan, Leyang Cui, Wenyang Gao, and Yue Zhang. 2022. FactMix: Using a Few Labeled In-domain Examples to Generalize to Cross-domain Named Entity Recognition. In Proceedings of the 29th International Conference on Computational Linguistics, pages 5360–5371, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- FactMix: Using a Few Labeled In-domain Examples to Generalize to Cross-domain Named Entity Recognition (Yang et al., COLING 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2022.coling-1.476.pdf
- Code
- lifan-yuan/factmix