Opportunities and Challenges of Natural Language Processing for Low-Resource Senegalese Languages in Social Science Research

TL;DR

This paper reviews NLP progress and challenges for Senegalese languages, highlighting data gaps, ongoing efforts, and potential for social science applications, aiming to foster inclusive, sustainable NLP ecosystems.

Contribution

It provides the first comprehensive overview of NLP for Senegalese languages, including a resource repository and a roadmap for community-centered development.

Findings

Identification of linguistic and infrastructural gaps

Compilation of NLP resources and ongoing efforts

Proposed roadmap for sustainable NLP ecosystems

Abstract

Natural Language Processing (NLP) is rapidly transforming research methodologies across disciplines, yet African languages remain largely underrepresented in this technological shift. This paper provides the first comprehensive overview of NLP progress and challenges for the six national languages officially recognized by the Senegalese Constitution: Wolof, Pulaar, Sereer, Joola, Mandingue, and Soninke. We synthesize linguistic, sociotechnical, and infrastructural factors that shape their digital readiness and identify gaps in data, tools, and benchmarks. Building on existing initiatives and research works, we analyze ongoing efforts in text normalization, machine translation, and speech processing. We also provide a centralized GitHub repository that compiles publicly accessible resources for a range of NLP tasks across these languages, designed to facilitate collaboration and reproducibility. A special focus is devoted to the application of NLP to the social sciences, where multilingual transcription, translation, and retrieval pipelines can significantly enhance the efficiency and inclusiveness of field research. The paper concludes by outlining a roadmap toward sustainable, community-centered NLP ecosystems for Senegalese languages, emphasizing ethical data governance, open resources, and interdisciplinary collaboration.