UQA：面向乌尔都语问答的语料库
UQA: Corpus for Urdu Question Answering
AI总结：
本文提出乌尔都语问答数据集UQA，通过EATS技术翻译SQuAD2.0并保留答案跨度，评测了翻译模型并在多语言模型上取得优异结果，为低资源语言NLP提供了宝贵资源。
摘要：
本文介绍了UQA，一个用于乌尔都语（一种拥有超过7000万母语者的低资源语言）问答和文本理解的新颖数据集。UQA是通过翻译Stanford Question Answering Dataset (SQuAD2.0)（一个大规模英语问答数据集）生成的，使用了一种名为EATS（Enclose to Anchor, Translate, Seek）的技术，该技术在翻译后的上下文段落中保留了答案跨度。本文描述了在两个候选模型：Google Translator和Seamless M4T中，选择和评估最佳翻译模型的过程。本文还在UQA上对几个最先进的多语言问答模型进行了基准测试，包括mBERT、XLM-RoBERTa和mT5，并报告了有希望的结果。对于XLM-RoBERTa-XL，我们获得了85.99的F1分数和74.56的EM。UQA是开发和测试乌尔都语多语言NLP系统以及增强现有模型跨语言可迁移性的宝贵资源。此外，本文证明了EATS在为其他语言和领域创建高质量数据集方面的有效性。UQA数据集和代码可在www.github.com/sameearif/UQA公开获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
