We Need to Measure Data Diversity in NLP - Better and Broader
Publication date
2025
Authors
Nguyen, Dong
Ploeger, Esther
Editors
Christodoulopoulos, Christos
Chakraborty, Tanmoy
Rose, Carolyn
Peng, Violet
Advisors
Supervisors
Document Type
Part of book
Metadata
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License
cc_by
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.
Keywords
Computational Theory and Mathematics, Computer Science Applications, Information Systems, Linguistics and Language
Citation
Nguyen, D & Ploeger, E 2025, We Need to Measure Data Diversity in NLP - Better and Broader. in C Christodoulopoulos, T Chakraborty, C Rose & V Peng (eds), EMNLP 2025 - 2025 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference. EMNLP 2025 - 2025 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference, Association for Computational Linguistics (ACL), pp. 8812-8821, 30th Conference on Empirical Methods in Natural Language Processing, EMNLP 2025, Suzhou, China, 4/11/25. https://doi.org/10.18653/v1/2025.emnlp-main.445, conference