Scholarlink

Objective <p>There is a critical scarcity of domain-specific, clinically grounded Natural Language Processing (NLP) resources for African languages. In Western Uganda, linguistic diversity creates a barrier to maternal healthcare, as mothers lack access to health information in their native languages. The objective of this dataset is to provide a high-quality, in-language medical corpus to enable the development and fine-tuning of Large Language Models (LLMs) and conversational AI tools for maternal health in resource-constrained settings.</p> Data description <p>The “Dialogues of Delivery” dataset is a multilingual, parallel corpus comprising 3,694 question-and-answer pairs presented in four languages: English, Luganda, Runyankore, and Swahili (14,800 total entries). Using facility-based convenience sampling at two health facilities in Western Uganda, primary data was collected via structured, open-ended questionnaires from 150 participants (expectant/postpartum mothers and maternal healthcare providers). The dataset underwent a rigorous forward-backward translation protocol by certified linguists and human-in-the-loop clinical validation by independent medical professionals. The dataset captures core maternal health domains, providing a culturally and clinically validated foundation for Afrocentric AI development.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Dialogues of delivery: a multilingual question-answer dataset for maternal healthcare in East African languages

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月2日

查看全文
  • Richard Kimera,
  • Rogers Kuyeso,
  • Emmanuel Maleka,
  • Fortunate Tukamushaba,
  • Wasswa William,
  • Rogers Mwavu,
  • David Sabiiti Bamutura,
  • Joseph Ngonzi,
  • Brenda Ainomugisha,
  • Alexer Namuli,
  • Proscovia Namiiro,
  • Clare Nyakato,
  • Leo Anthony Celi,
  • Fred Kaggwa

摘要

Objective

There is a critical scarcity of domain-specific, clinically grounded Natural Language Processing (NLP) resources for African languages. In Western Uganda, linguistic diversity creates a barrier to maternal healthcare, as mothers lack access to health information in their native languages. The objective of this dataset is to provide a high-quality, in-language medical corpus to enable the development and fine-tuning of Large Language Models (LLMs) and conversational AI tools for maternal health in resource-constrained settings.

Data description

The “Dialogues of Delivery” dataset is a multilingual, parallel corpus comprising 3,694 question-and-answer pairs presented in four languages: English, Luganda, Runyankore, and Swahili (14,800 total entries). Using facility-based convenience sampling at two health facilities in Western Uganda, primary data was collected via structured, open-ended questionnaires from 150 participants (expectant/postpartum mothers and maternal healthcare providers). The dataset underwent a rigorous forward-backward translation protocol by certified linguists and human-in-the-loop clinical validation by independent medical professionals. The dataset captures core maternal health domains, providing a culturally and clinically validated foundation for Afrocentric AI development.