CultureMINE: Datasets and Methods for Improving the Cultural Capabilities of NLP Systems

Abstract:In recent years, there has been a surge of interest in Cultural NLP, with substantial efforts to create globally inclusive NLP systems. The rapid growth of literature in this field makes it difficult to track trends in methods and data resources. To address this, we analyze over 375 papers to answer three complementary questions: (1) What Cultural Capabilities (CCs) are being targeted in NLP systems? (2) How are cultural data resources being created? and (3) What methods are being used to improve the CCs of those systems? We discuss trends observed across the three questions, and identify relevant research gaps. To facilitate further research in this field, we release our full list of analyzed papers in the form of an interactive web interface, which includes a feature to allow researchers to add their work; we hope this facilitates future research and proves to be a valuable resource for the Cultural NLP community.

Computation and Language (cs.CL)

arXiv:2609.22494 [cs.CL]

(or arXiv:2609.22494v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.22494 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

Proceedings of the Seventh Workshop on Natural Language Processing and Computational Social Science (2026), pp. 198-248

https://doi.org/10.18653/v1/2026.nlpcss-1.14 Focus to learn more DOI(s) linking to related resources

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.