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

TL;DR

The paper surveys NLP progress and challenges for Senegal's six national languages (Wolof, Pulaar, Sérère, Diola, Mandingue, Soninké) and their applicability to social science research. It synthesizes linguistic, sociotechnical, and infrastructural factors, and introduces a centralized GitHub repository of datasets, benchmarks, and tools to foster collaboration and reproducibility. The analysis covers core NLP tasks (parsing, tokenization, MT, speech) and highlights gaps, opportunities, and a roadmap toward sustainable, community-centered NLP ecosystems with ethical data governance. By outlining priority areas and practical steps, the paper aims to empower local researchers and policymakers to harness NLP for more inclusive social science research in Senegal.

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.

Figures (3)

• Figure 1: Contrast between the world's most spoken languages and their representation online.
• Figure 2: Proportion of speakers of the main local languages in terms of percentages leclerc2015.
• Figure 3: Main Senegalese languages and their locations in the country leclerc2015.