Towards Open-Ended Discovery for Low-Resource NLP
Bonaventure F. P. Dossou, Henri Aïdasso · 2025
Natural Language Processing (NLP) for lowresource languages remains fundamentally constrained by the lack of textual corpora, standardized orthographies, and scalable annotation pipelines.While recent advances in large language models have improved cross-lingual transfer, they remain inaccessible to underrepresented communities due to their reliance on massive, pre-collected data and centralized infrastructure.In this position paper, we argue for a paradigm shift toward open-ended, interactive language discovery, where AI systems learn new languages dynamically through dialogue rather than static datasets.We contend that the future of language technology, particularly for low-resource and under-documented languages, must move beyond static data collection pipelines toward interactive, uncertaintydriven discovery, where learning emerges dynamically from human-machine collaboration instead of being limited to pre-existing datasets.We propose a framework grounded in joint human-machine uncertainty, combining epistemic uncertainty from the model with hesitation cues and confidence signals from human speakers to guide interaction, query selection, and memory retention.This paper is a call to action: we advocate a rethinking of how AI engages with human knowledge in underdocumented languages, moving from extractive data collection toward participatory, coadaptive learning processes that respect and empower communities while discovering and preserving the world's linguistic diversity.This vision aligns with principles of human-centered AI, emphasizing interactive, cooperative model building between AI systems and speakers.