Year: 2026
Type: conference-paper
Abstract: Multilingual NLP has expanded rapidly through large-scale pretraining and cross-lingual transfer, yet this progress remains structurally uneven across writing systems.This survey reframes multilingual NLP around scripts rather than languages, arguing that writing systems constitute a critical and under-theorized axis of computational inequality.Focusing on African scripts-Indigenous (Vai, Ge'ez, Tifinagh), modern (ADLaM, N'Ko), and adapted Arabic-based (Ajami)-we analyze how script properties interact with digital infrastructure, tokenization, and downstream task performance.We organize the literature across four analytical layers: infrastructural (Unicode and input systems), representational (segmentation efficiency and vocabulary allocation), functional (task-level disparities), and epistemic (evaluation bias and the "low-resource" framing).Synthesizing evidence from 47 studies, we show that performance gaps across scripts arise primarily from engineering design choices rather than intrinsic linguistic complexity.We conclude by outlining a research agenda for native multiscript foundation models, including script-aware scaling laws, tokenizer equity metrics, and evaluation reform.We argue that multiscript equity is not a peripheral concern but a structural precondition for genuine multilingual inclusion.
Authors: Mamady Traore, Ngoc Tan Le, Fatiha Sadat
Institution: Université du Québec à Montréal
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Reduced inequalities
Open Access status: hybrid
OpenAlex ID: W7166564729