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Does synthetic data help named entity recognition for low-resource languages?

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DOIResolve DOI: https://doi.org/10.18653/v1/2025.ijcnlp-short.15
AuthorSearch for: 1; Search for: 1ORCID identifier: https://orcid.org/0000-0002-4033-9936
Affiliation
  1. National Research Council Canada. Digital Technologies
FormatText, Article
Conference14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics, December 2025, Mumbai, India
Abstract
We explore whether synthetic datasets generated by large language models using a few high quality seed samples are useful for low-resource named entity recognition, considering 11 languages from three language families. Our results suggest that synthetic data created with such seed data is a reasonable choice when there is no available labeled data, and is better than using entirely automatically labeled data. However, a small amount of high-quality data, coupled with cross-lingual transfer from a related language, always offers better performance.
Publication date
PublisherThe Asian Federation of Natural Language Processing
The Association for Computational Linguistics
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  • Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (2025): 159–167. .
LanguageEnglish
Peer reviewedYes
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Record identifier820f17f9-082b-47d5-a594-e098c50e3020
Record created2026-04-16
Record modified2026-05-27

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