MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NER

Ran Zhou, Xin Li, Ruidan He, Lidong Bing, Erik Cambria, Luo Si, Chunyan Miao


Abstract
Data augmentation is an effective solution to data scarcity in low-resource scenarios. However, when applied to token-level tasks such as NER, data augmentation methods often suffer from token-label misalignment, which leads to unsatsifactory performance. In this work, we propose Masked Entity Language Modeling (MELM) as a novel data augmentation framework for low-resource NER. To alleviate the token-label misalignment issue, we explicitly inject NER labels into sentence context, and thus the fine-tuned MELM is able to predict masked entity tokens by explicitly conditioning on their labels. Thereby, MELM generates high-quality augmented data with novel entities, which provides rich entity regularity knowledge and boosts NER performance. When training data from multiple languages are available, we also integrate MELM with code-mixing for further improvement. We demonstrate the effectiveness of MELM on monolingual, cross-lingual and multilingual NER across various low-resource levels. Experimental results show that our MELM consistently outperforms the baseline methods.
Anthology ID:
2022.acl-long.160
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2251–2262
Language:
URL:
https://aclanthology.org/2022.acl-long.160
DOI:
10.18653/v1/2022.acl-long.160
Bibkey:
Cite (ACL):
Ran Zhou, Xin Li, Ruidan He, Lidong Bing, Erik Cambria, Luo Si, and Chunyan Miao. 2022. MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NER. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2251–2262, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NER (Zhou et al., ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-long.160.pdf
Software:
 2022.acl-long.160.software.zip
Video:
 https://aclanthology.org/2022.acl-long.160.mp4
Code
 randyzhouran/melm