In this paper, we introduce Neural Information Retrieval resources for 11 widely spoken Indian Languages (Assamese, Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Oriya, Punjabi, Tamil, and Telugu) from two major Indian language families (Indo-Aryan and Dravidian). These resources include (a) INDIC-MARCO, a multilingual version of the MSMARCO dataset in 11 Indian Languages created using Machine Translation, and (b) Indic-ColBERT, a collection of 11 distinct Monolingual Neural Information Retrieval models, each trained on one of the 11 languages in the INDIC-MARCO dataset. To the best of our knowledge, IndicIRSuite is the first attempt at building large-scale Neural Information Retrieval resources for a large number of Indian languages, and we hope that it will help accelerate research in Neural IR for Indian Languages. Experiments demonstrate that Indic-ColBERT achieves 47.47% improvement in the MRR@10 score averaged over the INDIC-MARCO baselines for all 11 Indian languages except Oriya, 12.26% improvement in the NDCG@10 score averaged over the MIRACL Bengali and Hindi Language baselines, and 20% improvement in the MRR@100 Score over the Mr.Tydi Bengali Language baseline. IndicIRSuite is available at https://github.com/saifulhaq95/IndicIRSuite

翻译：本文介绍了针对印度两大语系（印度-雅利安语系和达罗毗荼语系）中11种广泛使用的印度语言（阿萨姆语、孟加拉语、古吉拉特语、印地语、卡纳达语、马拉雅拉姆语、马拉地语、奥里亚语、旁遮普语、泰米尔语和泰卢固语）的神经信息检索资源。这些资源包括：（a）INDIC-MARCO——基于机器翻译构建的11种印度语言的MSMARCO多语言版本数据集；（b）Indic-ColBERT——包含11个独立单语神经信息检索模型的集合，每个模型均针对INDIC-MARCO数据集中的一种语言单独训练。据我们所知，IndicIRSuite是首个为大量印度语言构建大规模神经信息检索资源的尝试，有望推动印度语言神经信息检索研究的发展。实验表明，除奥里亚语外，Indic-ColBERT在INDIC-MARCO基线基础上，所有11种印度语言的MRR@10平均得分提升47.47%；在MIRACL孟加拉语和印地语基线基础上，NDCG@10平均得分提升12.26%；在Mr.Tydi孟加拉语基线基础上，MRR@100得分提升20%。IndicIRSuite开源地址：https://github.com/saifulhaq95/IndicIRSuite