AfroScope: A Framework for Studying the Linguistic Landscape of Africa

TL;DR

AfroScope introduces a comprehensive framework for African language identification, including an extensive dataset and models, with hierarchical classification to improve distinctions among closely related languages, supporting large-scale linguistic analysis.

Contribution

The paper presents AfroScope, a unified framework with a large African language dataset and models, and a hierarchical classification approach for better differentiation of similar languages.

Findings

Hierarchical classification improves macro F1 by 4.55 on confusable languages

AfroScope covers 713 African languages with strong LID models

Analysis of cross-linguistic transfer informs robust system development

Abstract

Language Identification (LID) is the task of determining the language of a given text and is a fundamental preprocessing step that affects the reliability of downstream NLP applications. While recent work has expanded LID coverage for African languages, existing approaches remain limited in (i) the number of supported languages and (ii) their ability to make fine-grained distinctions among closely related varieties. We introduce AfroScope, a unified framework for African LID that includes AfroScope-Data, a dataset covering 713 African languages, and AfroScope-Models, a suite of strong LID models with broad language coverage. To better distinguish highly confusable languages, we propose a hierarchical classification approach that leverages Mirror-Serengeti, a specialized embedding model targeting 29 closely related or geographically proximate languages. This approach improves macro F1 by 4.55 on this confusable subset compared to our best base model. Finally, we analyze cross linguistic transfer and domain effects, offering guidance for building robust African LID systems. We position African LID as an enabling technology for large scale measurement of Africas linguistic landscape in digital text and release AfroScope-Data and AfroScope-Models publicly.