The absence of explicitly tailored, accessible annotated datasets for
educational purposes presents a notable obstacle for NLP tasks in languages
with limited resources.This study initially explores the feasibility of using
machine translation (MT) to convert an existing dataset into a Tigrinya dataset
in SQuAD format. As a result, we present TIGQA, an expert annotated educational
dataset consisting of 2.68K question-answer pairs covering 122 diverse topics
such as climate, water, and traffic. These pairs are from 537 context
paragraphs in publicly accessible Tigrinya and Biology books. Through
comprehensive analyses, we demonstrate that the TIGQA dataset requires skills
beyond simple word matching, requiring both single-sentence and
multiple-sentence inference abilities. We conduct experiments using
state-of-the art MRC methods, marking the first exploration of such models on
TIGQA. Additionally, we estimate human performance on the dataset and juxtapose
it with the results obtained from pretrained models.The notable disparities
between human performance and best model performance underscore the potential
for further enhancements to TIGQA through continued research. Our dataset is
freely accessible via the provided link to encourage the research community to
address the challenges in the Tigrinya MRC.

使用机器翻译将现有数据集转化为 Tigrinya 数据集，构建了 TIGQA 专家注释的教育数据集，通过综合分析展示了 TIGQA 数据集需要单句和多句推理能力，使用最先进的 MRC 方法进行了实验，并与人类表现进行对比，结果强调了对 TIGQA 的进一步研究增强的潜力。