This paper introduces UQA, a novel dataset for question answering and text
comprehension in Urdu, a low-resource language with over 70 million native
speakers. UQA is generated by translating the Stanford Question Answering
Dataset (SQuAD2.0), a large-scale English QA dataset, using a technique called
EATS (Enclose to Anchor, Translate, Seek), which preserves the answer spans in
the translated context paragraphs. The paper describes the process of selecting
and evaluating the best translation model among two candidates: Google
Translator and Seamless M4T. The paper also benchmarks several state-of-the-art
multilingual QA models on UQA, including mBERT, XLM-RoBERTa, and mT5, and
reports promising results. For XLM-RoBERTa-XL, we have an F1 score of 85.99 and
74.56 EM. UQA is a valuable resource for developing and testing multilingual
NLP systems for Urdu and for enhancing the cross-lingual transferability of
existing models. Further, the paper demonstrates the effectiveness of EATS for
creating high-quality datasets for other languages and domains. The UQA dataset
and the code are publicly available at www.github.com/sameearif/UQA.

介绍了 UQA，一个用于乌尔都语（Urdu）问答和文本理解的新数据集，通过一种称为 EATS 的技术，将斯坦福问答数据集（SQuAD2.0）的答案段落的翻译上下文中的答案范围进行保留，在两个候选项（Google Translator 和 Seamless M4T）中选择和评估最佳翻译模型，并对 UQA 上的几个最先进的多语言问答模型进行基准测试，其中包括 mBERT，XLM-RoBERTa 和 mT5，报告了有希望的结果。通过展示 EATS 对于创建其他语言和领域的高质量数据集的效果，证明了 UQA 对于开发和测试乌尔都语的多语言 NLP 系统以及增强现有模型的跨语言可迁移性是有价值的。UQA 数据集和代码可在 www.github.com/sameearif/UQA 上公开获取。