According to the global overview report from Digital 2023, nearly 82% of Internet users between 18 and 64 have used a search engine or web portal in the past month. Specifically, IR is the process of finding and providing relevant information in response to the user query from a large collection of data. Recently, with advances in deep learning, neural IR models have witnessed significant progress [51, 53]. With the development of training methodologies such as pre-training [44, 100] and fine-tuning [73, 117, 162], neural IR models have demonstrated remarkable effectiveness in learning query-document relevance patterns. Why is robustness important in IR? In real-world deployment of neural IR models, an aspect equally essential as their effectiveness is their robustness. A good IR system must not only exhibit high effectiveness under normal conditions but also demonstrate robustness in the face of abnormal conditions. The natural openness of IR systems makes them vulnerable to intrusion, and the consequences can be severe. For example: (i) Search engines are vulnerable to black hat SEO attacks, necessitating significant efforts to curb these infringements.

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

Pseudo-relevance feedback (PRF) is commonly used to boost the performance of traditional information retrieval (IR) models by using top-ranked documents to identify and weight new query terms, thereby reducing the effect of query-document vocabulary mismatches. While neural retrieval models have recently demonstrated strong results for ad-hoc retrieval, combining them with PRF is not straightforward due to incompatibilities between existing PRF approaches and neural architectures. To bridge this gap, we propose an end-to-end neural PRF framework that can be used with existing neural IR models by embedding different neural models as building blocks. Extensive experiments on two standard test collections confirm the effectiveness of the proposed NPRF framework in improving the performance of two state-of-the-art neural IR models.