Designing Culturally Inclusive NLP Models for Low-Resource Languages and Communities

Abstract

Over the last few years, Natural Language Processing has developed at an extremely fast pace, but the majority of these improvements focus on popular, well-resourced languages, which means that billions of speakers of under-resourced languages are not able to access modern language technologies. More to the point, the contemporary approaches to the development of Natural Language Processing do not tend to consider the social contexts, viewpoints, and requirements of the communities, the language of which is being modeled. The result of this is insufficient technologies that can even be detrimental to linguistic and cultural diversity. This paper examines the complex issues of planning culturally based Natural language Processing (NLP) for limited-resource languages and communities, aiming to transcend specific concerns to address fundamental questions about belief systems, power, representation, and local linguistic rights. This paper uncovers important features of equity in NLP through the analysis of linguistic diversity, technical constraints, and cultural dynamics. These include culturally based representations that reflect the language characteristics of marginalized languages, participatory design, which focuses on community knowledge and goals, cultural protocols, evaluation frameworks aligned with community values rather than Western academic standards, and sustainable methodologies that mitigate the effects of environmental and data extraction. Technical strategies, including transfer learning, multilingual language models, active learning, and few-shot learning, are also reviewed in the paper, though limitations of these methods are evaluated critically with respect to culturally different languages.