collaborators

7 papers

cs.CL2026

From Speech to Text Corpora: Evaluating ASR-Based Data Acquisition for Low-Resource Fongbe and Hausa

Mahounan Pericles Adjovi, Victor Olufemi, Roald Eiselen +1

Low-resource African languages lack text corpora needed for language model training. We investigate whether ASR pipelines can extend text resources for two typologically distinct W…

cs.CL2026

Evaluating Large Language Models for Hausa and Fongbe Machine Translation: Benchmarks, Failures, and Metric Reliability

Mahounan Pericles Adjovi, Roald Eiselen, Prasenjit Mitra

We investigate the translation quality of current large language models (LLMs) for English-to-Hausa and English-to-Fongbe - two typologically distinct West African languages from t…

cs.CL2026

When Does Data Augmentation Help? Evaluating LLM and Back-Translation Methods for Hausa and Fongbe NLP

Mahounan Pericles Adjovi, Roald Eiselen, Prasenjit Mitra

Data scarcity limits NLP development for low-resource African languages. We evaluate two data augmentation methods -- LLM-based generation (Gemini 2.5 Flash) and back-translation (…

cs.CL2026

Mining Large Language Models for Low-Resource Language Data: Comparing Elicitation Strategies for Hausa and Fongbe

Mahounan Pericles Adjovi, Roald Eiselen, Prasenjit Mitra

Large language models (LLMs) are trained on data contributed by low-resource language communities, yet the linguistic knowledge encoded in these models remains accessible only thro…

cs.CL2026

A Survey of Text and Speech Resources for Hausa and Fongbe: Availability, Quality, and Gaps for NLP Development

Mahounan Pericles Adjovi, Victor Olufemi, Roald Eiselen +1

This survey provides a comprehensive catalog of publicly available text and speech resources for two West African languages: Hausa, an Afroasiatic language with approximately 80-10…

cs.CL2026

Budget-Xfer: Budget-Constrained Source Language Selection for Cross-Lingual Transfer to African Languages

Tewodros Kederalah Idris, Roald Eiselen, Prasenjit Mitra

Cross-lingual transfer learning enables NLP for low-resource languages by leveraging labeled data from higher-resource sources, yet existing comparisons of source language selectio…