@inproceedings{gyamfi-etal-2026-synthetic,
title = "Synthetic Data Generation Pipeline for Low-Resource {S}wahili Sentiment Analysis: Multi-{LLM} Judging with Human Validation",
author = {Gyamfi, Samuel and
Kondoro, Alfred Malengo and
{\"O}zt{\"u}rk, Yank{\i} and
Schreiber, Richard Hans and
Borisov, Vadim},
editor = "Chimoto, Everlyn Asiko and
Lignos, Constantine and
Muhammad, Shamsuddeen and
Abdulmumin, Idris and
Siro, Clemencia and
Adelani, David Ifeoluwa",
booktitle = "Proceedings of the 7th Workshop on {A}frican Natural Language Processing ({A}frica{NLP} 2026)",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.africanlp-main.12/",
pages = "116--141",
ISBN = "979-8-89176-364-7",
abstract = "Despite serving over 100 million speakers as a vital African lingua franca, Swahili remains critically under-resourced for Natural Language Processing, hindering technological progress across East Africa. We present a scalable solution: a controllable synthetic data generation pipeline that produces culturally grounded Swahili text for sentiment analysis, validated through automated LLM judges. To ensure reliability, we conduct targeted human evaluation with a native Swahili speaker on a stratified sample, achieving 80.95{\%} agreementbetween generated sentiment labels and human ground truth, with strong agreement on judge quality assessments. This demonstrates that LLM-based generation and quality assessment can transfer effectively to low-resource languages. We release a dataset and provide a reproducible pipeline in tandem, providing ample knowledge and working material for NLP researchers in low-resource contexts. We release the resulting Swahili sentiment dataset and the full reproducible generation pipeline publicly at https://huggingface.co/datasets/tabularisai/swahili-sentiment-dataset and https://github.com/tabularis-ai/Synthetic-Data-Generation-Pipeline-for-Low-Resource-Swahili-Sentiment-Analysis."
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<title>Proceedings of the 7th Workshop on African Natural Language Processing (AfricaNLP 2026)</title>
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<abstract>Despite serving over 100 million speakers as a vital African lingua franca, Swahili remains critically under-resourced for Natural Language Processing, hindering technological progress across East Africa. We present a scalable solution: a controllable synthetic data generation pipeline that produces culturally grounded Swahili text for sentiment analysis, validated through automated LLM judges. To ensure reliability, we conduct targeted human evaluation with a native Swahili speaker on a stratified sample, achieving 80.95% agreementbetween generated sentiment labels and human ground truth, with strong agreement on judge quality assessments. This demonstrates that LLM-based generation and quality assessment can transfer effectively to low-resource languages. We release a dataset and provide a reproducible pipeline in tandem, providing ample knowledge and working material for NLP researchers in low-resource contexts. We release the resulting Swahili sentiment dataset and the full reproducible generation pipeline publicly at https://huggingface.co/datasets/tabularisai/swahili-sentiment-dataset and https://github.com/tabularis-ai/Synthetic-Data-Generation-Pipeline-for-Low-Resource-Swahili-Sentiment-Analysis.</abstract>
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%0 Conference Proceedings
%T Synthetic Data Generation Pipeline for Low-Resource Swahili Sentiment Analysis: Multi-LLM Judging with Human Validation
%A Gyamfi, Samuel
%A Kondoro, Alfred Malengo
%A Öztürk, Yankı
%A Schreiber, Richard Hans
%A Borisov, Vadim
%Y Chimoto, Everlyn Asiko
%Y Lignos, Constantine
%Y Muhammad, Shamsuddeen
%Y Abdulmumin, Idris
%Y Siro, Clemencia
%Y Adelani, David Ifeoluwa
%S Proceedings of the 7th Workshop on African Natural Language Processing (AfricaNLP 2026)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-364-7
%F gyamfi-etal-2026-synthetic
%X Despite serving over 100 million speakers as a vital African lingua franca, Swahili remains critically under-resourced for Natural Language Processing, hindering technological progress across East Africa. We present a scalable solution: a controllable synthetic data generation pipeline that produces culturally grounded Swahili text for sentiment analysis, validated through automated LLM judges. To ensure reliability, we conduct targeted human evaluation with a native Swahili speaker on a stratified sample, achieving 80.95% agreementbetween generated sentiment labels and human ground truth, with strong agreement on judge quality assessments. This demonstrates that LLM-based generation and quality assessment can transfer effectively to low-resource languages. We release a dataset and provide a reproducible pipeline in tandem, providing ample knowledge and working material for NLP researchers in low-resource contexts. We release the resulting Swahili sentiment dataset and the full reproducible generation pipeline publicly at https://huggingface.co/datasets/tabularisai/swahili-sentiment-dataset and https://github.com/tabularis-ai/Synthetic-Data-Generation-Pipeline-for-Low-Resource-Swahili-Sentiment-Analysis.
%U https://aclanthology.org/2026.africanlp-main.12/
%P 116-141
Markdown (Informal)
[Synthetic Data Generation Pipeline for Low-Resource Swahili Sentiment Analysis: Multi-LLM Judging with Human Validation](https://aclanthology.org/2026.africanlp-main.12/) (Gyamfi et al., AfricaNLP 2026)
ACL