arXiv subjects

Dennis Owusu

Publications and source records attributed to Dennis Owusu.

3 recordsLinked to original sources

arXiv2026

A Systematic Review of NLP for Ghanaian Languages: Datasets, Models, and a Research Roadmap

Natural Language Processing (NLP) for Ghana's 73 living indigenous languages remains deeply fragmented, under-resourced, and heavily skewed toward a single language. We present the first systematic review of the Ghanaian NLP landscape, screening 17,000+ publications across four academic databases to critically synthesize 36 core studies spanning datasets, model architectures, and evaluation paradigms. Our analysis exposes a severe resource imbalance: Twi-centric NLP has grown modestly, driven largely by religious-text alignment and crowdsourcing, while the remaining 70+ languages remain almost entirely unaddressed, and dataset releases, model checkpoints, and evaluation practices remain inconsistent and rarely shared across the field. We translate these findings into a prioritized roadmap targeting Ghana's acute regional constraints, dialectal variation, non-standardized orthographies, and the absence of shared infrastructure, offering a replicable template for systematic review and research prioritization in other low-resource language settings.

cs.CL↗

arXiv2026

Global PIQA: Evaluating Commonsense Reasoning Across 100+ Languages and Cultures

To date, there exist almost no culturally-specific evaluation benchmarks for large language models (LLMs) that cover a large number of languages and cultures. In this paper, we present Global PIQA, a participatory commonsense reasoning benchmark for over 100 languages, constructed by hand by over 350 researchers from over 65 countries around the world. The 141 language varieties in Global PIQA cover five continents, 19 language families, and 24 writing systems. In the non-parallel split of Global PIQA, over 50% of examples reference local foods, customs, traditions, or other culturally-specific elements. In the parallel split, we translate more "culturally agnostic" commonsense reasoning questions into 131 language varieties, for direct cross-lingual comparisons. In both splits, all examples have been verified by native speakers of the languages. We find that state-of-the-art LLMs perform well on Global PIQA in aggregate, but they exhibit weaker performance in lower-resource languages (e.g. up to a 68% accuracy gap between languages in the parallel split). Global PIQA highlights that in many languages and cultures, everyday knowledge remains an area for improvement in LLMs, alongside more widely-discussed capabilities such as complex reasoning and expert knowledge. Beyond its uses for LLM evaluation, Global PIQA provides a glimpse into the wide diversity of cultures in which human language is embedded.

cs.CL↗

arXiv2026

Grammatical Error Correction for Low-Resource Languages: The Case of Zarma

Grammatical error correction (GEC) aims to improve text quality and readability. Previous work on the task focused primarily on high-resource languages, while low-resource languages lack robust tools. To address this shortcoming, we present a study on GEC for Zarma, a language spoken by over five million people in West Africa. We compare three approaches: rule-based methods, machine translation (MT) models, and large language models (LLMs). We evaluated GEC models using a dataset of more than 250,000 examples, including synthetic and human-annotated data. Our results showed that the MT-based approach using M2M100 outperforms others, with a detection rate of 95.82% and a suggestion accuracy of 78.90% in automatic evaluations (AE) and an average score of 3.0 out of 5.0 in manual evaluation (ME) from native speakers for grammar and logical corrections. The rule-based method was effective for spelling errors but failed on complex context-level errors. LLMs -- Gemma 2b and MT5-small -- showed moderate performance. Our work supports use of MT models to enhance GEC in low-resource settings, and we validated these results with Bambara, another West African language.

cs.CL↗