| Challenge: | Several important texts which are of interest to people all over the world were written in Sanskrit. |
| Approach: | They develop a Sanskrit benchmark to evaluate the completeness and accuracy of tools . they use three most prominent tools to evaluate their completeness . |
| Outcome: | The proposed tools have substantial scope for improvement and are available to researchers worldwide. |
Similar Papers
A Benchmark and Dataset for Post-OCR text correction in Sanskrit (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Sanskrit is a classical language with 30 million manuscripts available for digitisation . however, it is considered to be low-resource when it comes to available digital resources. |
| Approach: | They propose to use a post-OCR text correction dataset to correct errors from OCR predictions from 30 different books in the Indian subcontinent. |
| Outcome: | The proposed model outperforms OCR models on graphemic and lexical levels and shows that it is more accurate than previous models. |
SANSKRITI: A Comprehensive Benchmark for Evaluating Language Models’ Knowledge of Indian Culture (2025.findings-acl)
Copied to clipboard
| Challenge: | Language models excel in syntactic and semantic analysis, while small language models struggle in region-specific contexts. |
| Approach: | They evaluate SANSKRITI on leading Large Language Models, Indic Language Model, and Small Language Model (SLM) it covers 16 key attributes of Indian culture including rituals and ceremonies, history, tourism, cuisine, dance and music, costume, language, art, festivals, religion, medicine, transport, sports, nightlife and personalities. |
| Outcome: | The SANSKRITI dataset covers 16 attributes of Indian culture . it reveals that many models struggle in region-specific contexts . |
The Treebank of Vedic Sanskrit (2020.lrec-1)
Copied to clipboard
| Challenge: | Vedic Sanskrit is a morphologically rich ancient Indian language of central importance for linguistic and historical research. |
| Approach: | They introduce the first treebank of Vedic Sanskrit, a morphologically rich ancient Indian language . they describe how sentences are annotated in the Universal Dependencies scheme and which syntactic constructions required special attention. |
| Outcome: | The proposed treebank reflects the development of metrical and prose texts over a period of 600 years. |
Samayik: A Benchmark and Dataset for English-Sanskrit Translation (2024.lrec-main)
Copied to clipboard
Ayush Maheshwari, Ashim Gupta, Amrith Krishna, Atul Kumar Singh, Ganesh Ramakrishnan, Anil Kumar Gourishetty, Jitin Singla
| Challenge: | Existing Sanskrit corpora focus on poetry and offer limited coverage of contemporary written materials. |
| Approach: | They release a dataset of 53,000 parallel English-Sanskrit sentences . they use spoken content that covers contemporary world affairs and interpretations . |
| Outcome: | a new dataset of 53,000 parallel English-Sanskrit sentences is released . the dataset outperforms existing models trained on older classical-era poetry datasets . |
Building a Word Segmenter for Sanskrit Overnight (L18-1)
Copied to clipboard
| Challenge: | Sanskrit word segmentation is challenging due to the issue of Sandhi . digitisation efforts have made the manuscripts available in the public domain . |
| Approach: | They propose a deep sequence to sequence model that takes only the sandhied string as input and predicts the unsandhized string. |
| Outcome: | The proposed model improves on the current state of the art by 16.79% . the system can be trained "overnight" and be used for production . |
IL-TUR: Benchmark for Indian Legal Text Understanding and Reasoning (2024.acl-long)
Copied to clipboard
| Challenge: | Legal systems worldwide struggle with exponentially growing legal cases in various courts. |
| Approach: | They propose a benchmark for Indian legal text understanding and reasoning task that includes domain-specific tasks that address different aspects of the legal system. |
| Outcome: | The proposed benchmark for Indian legal text understanding and reasoning aims to address the gap between models and the ground truth. |
Sanskrit Voyager: Unified Web Platform for Interactive Reading and Linguistic Analysis of Sanskrit Texts (2025.emnlp-demos)
Copied to clipboard
| Challenge: | Sanskrit Voyager enables users to search for words and phrases as they actually appear in texts . evaluation shows over 92% parsing accuracy on complex compounds compared to BuddhaNexus . |
| Approach: | Sanskrit Voyager is a web application for searching, reading and analyzing the Sanskrt literary corpus. |
| Outcome: | Sanskrit Voyager is a web application for searching, reading, and analyzing the Sanskrt literary corpus. |
Sanskrit Sandhi Splitting using seq2(seq)2 (D18-1)
Copied to clipboard
| Challenge: | Existing methods for word splitting in Sanskrit have low accuracy as the same compound word might be broken down in multiple ways to provide syntactically correct splits. |
| Approach: | They propose a deep learning architecture called Double Decoder RNN which predicts the location of the splits with 95% accuracy and 79.5% accuracy. |
| Outcome: | The proposed model outperforms the state-of-the-art in the problem of Chinese word segmentation with 79.5% accuracy and the existing model's generalization capability. |
Automatic Speech Recognition in Sanskrit: A New Speech Corpus and Modelling Insights (2021.findings-acl)
Copied to clipboard
| Challenge: | In this paper, we propose the first large scale study of automatic speech recognition in Sanskrit . we focus on the impact of unit selection in San's ASR systems . |
| Approach: | They propose a large scale study of automatic speech recognition in Sanskrit . they propose syllable level unit selection that captures character sequences . |
| Outcome: | The proposed model captures character sequences from one vowel in the word to the next vowela. |
SanskritShala: A Neural Sanskrit NLP Toolkit with Web-Based Interface for Pedagogical and Annotation Purposes (2023.acl-demo)
Copied to clipboard
| Challenge: | SanskritShala is a neural-based Sanskrit NLP toolkit that is available as a web-based application . |
| Approach: | They propose a neural Sanskrit NLP toolkit that facilitates linguistic analyses for word segmentation, morphological tagging, dependency parsing, and compound type identification. |
| Outcome: | The proposed toolkit reports state-of-the-art performance on benchmark datasets . it is built with easy-to-use interactive data annotation features . |