As the Information Retrieval (IR) field increasingly recognizes the importance of inclusivity, addressing the needs of low-resource languages remains a significant challenge. This paper introduces the first large-scale Urdu IR dataset, created by translating the MS MARCO dataset through machine translation. We establish baseline results through zero-shot learning for IR in Urdu and subsequently apply the mMARCO multilingual IR methodology to this newly translated dataset. Our findings demonstrate that the fine-tuned model (Urdu-mT5-mMARCO) achieves a Mean Reciprocal Rank (MRR@10) of 0.247 and a Recall@10 of 0.439, representing significant improvements over zero-shot results and showing the potential for expanding IR access for Urdu speakers. By bridging access gaps for speakers of low-resource languages, this work not only advances multilingual IR research but also emphasizes the ethical and societal importance of inclusive IR technologies. This work provides valuable insights into the challenges and solutions for improving language representation and lays the groundwork for future research, especially in South Asian languages, which can benefit from the adaptable methods used in this study.

翻译：随着信息检索领域日益认识到包容性的重要性，满足低资源语言的需求仍然是一项重大挑战。本文介绍了首个大规模乌尔都语信息检索数据集，该数据集通过机器翻译MS MARCO数据集构建而成。我们通过零样本学习为乌尔都语信息检索建立了基准结果，随后将mMARCO多语言信息检索方法应用于这一新翻译的数据集。研究结果表明，经过微调的模型在平均倒数排名上达到0.247，在召回率上达到0.439，相较于零样本结果有显著提升，并展现了为乌尔都语使用者扩展信息检索访问途径的潜力。通过弥合低资源语言使用者的访问鸿沟，这项工作不仅推动了多语言信息检索研究的发展，更强调了包容性信息检索技术所承载的伦理与社会重要性。本研究为改善语言表征所面临的挑战及解决方案提供了宝贵见解，并为未来研究奠定了基础，尤其对南亚语言的研究具有借鉴意义——这些语言可受益于本研究所采用的适应性方法。