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DataDivers

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Diving into Data Diversity for Fair and Robust Natural Language Processing

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... ver más

31/12/2029

2M€

Presupuesto del proyecto: 2M€

Líder del proyecto

UNIVERSITEIT UTRECHT No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2024-10-15

ERC-2024-STG: ERC STARTING GRANTS Scope:Objectives

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1 Participantes

1.50M€ | Lider

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Duración del proyecto: 62 meses Fecha Inicio: 2024-10-15
Fecha Fin: 2029-12-31

Líder del proyecto

UNIVERSITEIT UTRECHT No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 2M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto 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. It is both surprising and concerning that we still lack the tools and theoretical insights to understand, improve, and leverage data diversity in NLP. DataDivers will 1) develop the first ever 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. I operationally define the diversity of a text collection as the variability of texts along specific dimensions (e.g., semantic, lexical, and sociolinguistic). Sociolinguistic diversity in particular, is an overlooked but crucial dimension, which I am committed to addressing. DataDivers will break new ground by taking a comprehensive view of data diversity, which is urgently needed for robust and fair NLP. Its approach will be both theoretical and empirical. It will combine insights from disciplines that have developed methodologies to quantify data diversity with rigorous empirical experimentation. DataDivers will take a unique view on data diversity: measuring it at the dataset level, and across contexts for individual features. Finally, DataDivers will use its framework to develop diversity-informed data collection and model training methods. DataDivers’ results will impact the full NLP development pipeline—from data collection to evaluation—and open up a new, urgently needed, area of research.

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