DataDivers: Diving into Data Diversity
for Fair and Robust NLP

Summary

Despite great progress in the field of Natural Language Processing (NLP), the field is still struggling to ensure the robustness and fairness of models. So far, NLP has prioritized data size over data quality. Yet there is growing evidence suggesting that the diversity of data, a key dimension of data quality, is crucial for fair and robust NLP models. Many researchers are therefore trying to create more diverse datasets, but there is no clear path for them to follow. Even the fundamental question “How can we measure the diversity of a dataset? ” is currently wide open. We still lack the tools and theoretical insights to understand, improve, and leverage data diversity in NLP.

DataDivers will 1) develop a framework to measure data diversity in NLP datasets; 2) investigate how data diversity impacts NLP model behavior; and 3) develop novel approaches that harness data diversity for fairer and more robust NLP models.