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        <title>We Need to Measure Data Diversity in NLP — Better and Broader</title>
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    <abstract>Although diversity in NLP datasets has received growing attention, the question of how to measure it remains largely underexplored. This opinion paper examines the conceptual and methodological challenges of measuring data diversity and argues that interdisciplinary perspectives are essential for developing more fine-grained and valid measures.</abstract>
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%0 Conference Proceedings
%T We Need to Measure Data Diversity in NLP — Better and Broader
%A Nguyen, Dong
%A Ploeger, Esther
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F nguyen-ploeger-2025-need
%X Although diversity in NLP datasets has received growing attention, the question of how to measure it remains largely underexplored. This opinion paper examines the conceptual and methodological challenges of measuring data diversity and argues that interdisciplinary perspectives are essential for developing more fine-grained and valid measures.
%U https://aclanthology.org/2025.emnlp-main.445/
%P 8823-8832

Markdown (Informal)

[We Need to Measure Data Diversity in NLP — Better and Broader](https://aclanthology.org/2025.emnlp-main.445/) (Nguyen & Ploeger, EMNLP 2025)

• We Need to Measure Data Diversity in NLP — Better and Broader (Nguyen & Ploeger, EMNLP 2025)

ACL

• Dong Nguyen and Esther Ploeger. 2025. We Need to Measure Data Diversity in NLP — Better and Broader. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 8823–8832, Suzhou, China. Association for Computational Linguistics.