Does Synthetic Data Help Named Entity Recognition for Low-Resource Languages?

arXiv:2505.16814 · cs.CL · Submitted 2025-05-22 · Read on arXiv

cs.CL

Submitted: 2025-05-22

Updated: 2025-11-05

Comments: Accepted at AACL 2025. Camera-ready version

Journal ref: https://aclanthology.org/2025.ijcnlp-short.15/

DOI: 10.18653/v1/2025.ijcnlp-short.15

Code: https://github.com/grvkamath/low-resource-syn-ner

License: http://creativecommons.org/licenses/by-sa/4.0/

The gist: Named Entity Recognition(NER) for low-resource languages aims to produce robust systems for languages where there is limited labeled training data available, and has been an area of increasing

Terminology

Abstract

Named Entity Recognition(NER) for low-resource languages aims to produce robust systems for languages where there is limited labeled training data available, and has been an area of increasing interest within NLP. Data augmentation for increasing the amount of low-resource labeled data is a common practice. In this paper, we explore the role of synthetic data in the context of multilingual, low-resource NER, considering 11 languages from diverse language families. Our results suggest that synthetic data does in fact hold promise for low-resource language NER, though we see significant variation between languages.

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