Data Augmentation for Low-Resource Named Entity Recognition Using Backtranslation

Usama Yaseen, Stefan Langer


Abstract
The state of art natural language processing systems relies on sizable training datasets to achieve high performance. Lack of such datasets in the specialized low resource domains lead to suboptimal performance. In this work, we adapt backtranslation to generate high quality and linguistically diverse synthetic data for low-resource named entity recognition. We perform experiments on two datasets from the materials science (MaSciP) and biomedical (S800) domains. The empirical results demonstrate the effectiveness of our proposed augmentation strategy, particularly in the low-resource scenario.
Anthology ID:
2021.icon-main.43
Volume:
Proceedings of the 18th International Conference on Natural Language Processing (ICON)
Month:
December
Year:
2021
Address:
National Institute of Technology Silchar, Silchar, India
Venue:
ICON
SIG:
Publisher:
NLP Association of India (NLPAI)
Note:
Pages:
352–358
Language:
URL:
https://aclanthology.org/2021.icon-main.43
DOI:
Bibkey:
Cite (ACL):
Usama Yaseen and Stefan Langer. 2021. Data Augmentation for Low-Resource Named Entity Recognition Using Backtranslation. In Proceedings of the 18th International Conference on Natural Language Processing (ICON), pages 352–358, National Institute of Technology Silchar, Silchar, India. NLP Association of India (NLPAI).
Cite (Informal):
Data Augmentation for Low-Resource Named Entity Recognition Using Backtranslation (Yaseen & Langer, ICON 2021)
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PDF:
https://aclanthology.org/2021.icon-main.43.pdf