TantraTagger: A Benchmark Dataset for Tantrayukti-Based Discourse Structure Labelling in Sanskrit Śāstra Texts

2026-06-08 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, quick

Summary

"TantraTagger" is a newly introduced benchmark dataset specifically developed for Tantrayukti-based discourse structure labelling in Sanskrit Śāstra Texts. Authored by Tapas Khanra, Priya Mishra, Malhar Kulkarni, and Ganesh Ramakrishnan, this dataset provides a critical resource for computational linguists and Sanskrit scholars. It aims to facilitate the automated analysis of complex argumentative and structural components found within ancient Indian philosophical and scientific treatises. The research was presented at the 8th International Sanskrit Computational Linguistics Symposium (ISCLS) in March 2026, held at IIT Roorkee, India. Published by the Association for Computational Linguistics, the paper detailing TantraTagger spans pages 47–64 of the symposium's proceedings, offering a standardized tool for advancing research in this specialized domain.

Key takeaway

For NLP engineers and research scientists focused on historical or low-resource languages, particularly Sanskrit, the "TantraTagger" dataset offers a crucial new resource. If you are developing models for discourse structure analysis in Sanskrit Śāstra Texts, you should evaluate this benchmark. It provides a standardized foundation for Tantrayukti-based labelling, potentially accelerating progress in automated understanding of complex ancient Indian texts and enabling more robust model development.

Key insights

TantraTagger is a benchmark dataset for discourse structure labelling in Sanskrit Śāstra Texts.

Topics

• TantraTagger
• Benchmark Datasets
• Discourse Analysis
• Sanskrit NLP
• Computational Linguistics
• Text Annotation

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.