Entity-to-Text based Data Augmentation for various Named Entity Recognition Tasks

Xuming Hu, Yong Jiang, Aiwei Liu, Zhongqiang Huang, Pengjun Xie, Fei Huang, Lijie Wen, Philip S. Yu

Abstract
Data augmentation techniques have been used to alleviate the problem of scarce labeled data in various NER tasks (flat, nested, and discontinuous NER tasks). Existing augmentation techniques either manipulate the words in the original text that break the semantic coherence of the text, or exploit generative models that ignore preserving entities in the original text, which impedes the use of augmentation techniques on nested and discontinuous NER tasks. In this work, we propose a novel Entity-to-Text based data augmentation technique named EnTDA to add, delete, replace or swap entities in the entity list of the original texts, and adopt these augmented entity lists to generate semantically coherent and entity preserving texts for various NER tasks. Furthermore, we introduce a diversity beam search to increase the diversity during the text generation process. Experiments on thirteen NER datasets across three tasks (flat, nested, and discontinuous NER tasks) and two settings (full data and low resource settings) show that EnTDA could bring more performance improvements compared to the baseline augmentation techniques.
Anthology ID:
2023.findings-acl.578
Volume:
Findings of the Association for Computational Linguistics: ACL 2023
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
9072–9087
Language:
URL:
https://aclanthology.org/2023.findings-acl.578
DOI:
10.18653/v1/2023.findings-acl.578
Bibkey:
Cite (ACL):
Xuming Hu, Yong Jiang, Aiwei Liu, Zhongqiang Huang, Pengjun Xie, Fei Huang, Lijie Wen, and Philip S. Yu. 2023. Entity-to-Text based Data Augmentation for various Named Entity Recognition Tasks. In Findings of the Association for Computational Linguistics: ACL 2023, pages 9072–9087, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
Entity-to-Text based Data Augmentation for various Named Entity Recognition Tasks (Hu et al., Findings 2023)
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https://preview.aclanthology.org/nschneid-patch-5/2023.findings-acl.578.pdf
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