2025
EMNLP
EMNLP 2025
We Need to Measure Data Diversity in NLP — Better and Broader
Abstract
AbstractAlthough 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.
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Interdisciplinary Bridge
— Data Science & Analytics and Interdisciplinary and Machine Learning and Natural Language Processing
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Keyword Pioneer
— dataset measurement
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy, Speech & Audio