首个乌尔都语大规模检索数据集，提升低资源语言信息获取能力
Enabling Low-Resource Language Retrieval: Establishing Baselines for Urdu MS MARCO
- 用机器翻译构建首个乌尔都语大规模检索数据集
- 微调模型达MRR@10 0.247，召回率@10为0.439
- 为南亚低资源语言检索提供可复用方法
随着信息检索（IR）领域日益重视包容性，低资源语言的需求仍面临重大挑战。本文首次构建大规模乌尔都语信息检索数据集，通过机器翻译将MS MARCO数据集转换而来。我们采用零样本学习建立基线，并应用mMARCO多语言检索方法于该新数据集。结果表明，微调模型（Urdu-mT5-mMARCO）在MRR@10上达到0.247，召回率@10为0.439，显著优于零样本表现，展现出提升乌尔都语使用者信息获取的潜力。本研究不仅推动多语言信息检索发展，更强调包容性技术的伦理与社会价值。工作为未来南亚语言研究奠定基础，所用方法具备良好适应性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。