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arXiv · 2505.19494

Anveshana: A New Benchmark Dataset for Cross-Lingual Information Retrieval On English Queries and Sanskrit Documents

Manoj Balaji Jagadeeshan·Prince Raj·Pawan Goyal
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Abstract

The study presents a comprehensive benchmark for retrieving Sanskrit documents using English queries, focusing on the chapters of the Srimadbhagavatam. It employs a tripartite approach: Direct Retrieval (DR), Translation-based Retrieval (DT), and Query Translation (QT), utilizing shared embedding spaces and advanced translation methods to enhance retrieval systems in a RAG framework. The study fine-tunes state-of-the-art models for Sanskrit's linguistic nuances, evaluating models such as BM25, REPLUG, mDPR, ColBERT, Contriever, and GPT-2. It adapts summarization techniques for Sanskrit documents to improve QA processing. Evaluation shows DT methods outperform DR and QT in handling the cross-lingual challenges of ancient texts, improving accessibility and understanding. A dataset of 3,400 English-Sanskrit query-document pairs underpins the study, aiming to preserve Sanskrit scriptures and share their philosophical importance widely. Our dataset is publicly available at https://huggingface.co/datasets/manojbalaji1/anveshana

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Manoj Balaji Jagadeeshan, Prince Raj, Pawan Goyal. 2025-05-26. Anveshana: A New Benchmark Dataset for Cross-Lingual Information Retrieval On English Queries and Sanskrit Documents. https://arxiv.org/abs/2505.19494

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