Papers with Sanskrit
SanskritShala: A Neural Sanskrit NLP Toolkit with Web-Based Interface for Pedagogical and Annotation Purposes (2023.acl-demo)
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| 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 . |
Sanskrit Voyager: Unified Web Platform for Interactive Reading and Linguistic Analysis of Sanskrit Texts (2025.emnlp-demos)
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| 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. |
Chandomitra: Towards Generating Structured Sanskrit Poetry from Natural Language Inputs (2026.eacl-long)
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Manoj Balaji Jagadeeshan, Samarth Bhatia, Pretam Ray, Harshul Raj Surana, Akhil Rajeev P, Priya Mishra, Annarao Kulkarni, Ganesh Ramakrishnan, Prathosh Ap, Pawan Goyal
| Challenge: | Large language models are capable of creative generation tasks but prominently for high-resource languages. |
| Approach: | They propose to use large language models for structured poetry generation in Sanskrit . their constrained decoding method achieves 99.86% syntactic accuracy . |
| Outcome: | The proposed model outperforms the existing model in generating metrically valid Sanskrit poetry. |
Poetry to Prose Conversion in Sanskrit as a Linearisation Task: A Case for Low-Resource Languages (P19-1)
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| Challenge: | Obtaining the proper word ordering, called as the prose ordering, from a verse is often considered a task which requires linguistic expertise. |
| Approach: | They propose a word ordering (linearisation) task that ignores the word arrangement at the verse side. |
| Outcome: | The proposed model outperforms current models in word ordering for the translation task in Sanskrit. |
Free as in Free Word Order: An Energy Based Model for Word Segmentation and Morphological Tagging in Sanskrit (D18-1)
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Amrith Krishna, Bishal Santra, Sasi Prasanth Bandaru, Gaurav Sahu, Vishnu Dutt Sharma, Pavankumar Satuluri, Pawan Goyal
| Challenge: | a structured prediction framework is proposed to solve word segmentation and morphological tagging tasks in a free word order language. |
| Approach: | They propose a structured prediction framework that jointly solves word segmentation and morphological tagging tasks in Sanskrit. |
| Outcome: | The proposed model outperforms the state of the art with an F-Score of 96.92 (percentage improvement of 7.06%) while using less than one tenth of the task-specific training data. |
Sanskrit Word Segmentation Using Character-level Recurrent and Convolutional Neural Networks (D18-1)
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| Challenge: | Using end-to-end neural network models, Sanskrit is tokenized by splitting compounds and resolving phonetic merges. |
| Approach: | They propose end-to-end neural network models that tokenize Sanskrit by jointly splitting compounds and resolving phonetic merges. |
| Outcome: | The proposed models outperform the state-of-the-art for the task of splitting compounds and resolving phonetic merges. |
Keep it Surprisingly Simple: A Simple First Order Graph Based Parsing Model for Joint Morphosyntactic Parsing in Sanskrit (2020.emnlp-main)
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| Challenge: | Morphologically rich languages benefit from joint processing of morphology and syntax, as compared to pipeline architectures. |
| Approach: | They propose a graph-based model for joint morphological parsing and dependency parser in Sanskrit using the Energy based model framework. |
| Outcome: | The proposed model outperforms standalone morphological parsers in morphology and syntax parsing, and in dependency parser. |
Automatic Speech Recognition in Sanskrit: A New Speech Corpus and Modelling Insights (2021.findings-acl)
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| 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. |
A Benchmark and Dataset for Post-OCR text correction in Sanskrit (2022.findings-emnlp)
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| 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. |
SandhiKosh: A Benchmark Corpus for Evaluating Sanskrit Sandhi Tools (L18-1)
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| 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. |
SHR++: An Interface for Morpho-syntactic Annotation of Sanskrit Corpora (2020.lrec-1)
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| Challenge: | SHR++ is designed to generate annotations for word segmentation, morphological parsing and dependency analysis tasks in Sanskrit. |
| Approach: | They propose a web-based annotation framework, SHR++, for morpho-syntactic annotation of corpora in Sanskrit. |
| Outcome: | The proposed framework reduces the time spent on the annotation tasks by 20.15 %. |
DepNeCTI: Dependency-based Nested Compound Type Identification for Sanskrit (2023.findings-emnlp)
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Jivnesh Sandhan, Yaswanth Narsupalli, Sreevatsa Muppirala, Sriram Krishnan, Pavankumar Satuluri, Amba Kulkarni, Pawan Goyal
| Challenge: | Multi-component compounding is a prevalent phenomenon in Sanskrit, and understanding the implicit structure of a compound is crucial for deciphering its meaning. |
| Approach: | They propose a task to identify nested spans of a multi-component compound and decode the implicit semantic relations between them. |
| Outcome: | The proposed framework surpasses the best baseline framework with an average improvement of 13.1 points in terms of Labeled Span Score and 5-fold enhancement in inference efficiency. |
Samayik: A Benchmark and Dataset for English-Sanskrit Translation (2024.lrec-main)
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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 . |
Mahānāma: A Unique Testbed for Literary Entity Discovery and Linking (2025.emnlp-main)
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| Challenge: | High lexical variation, ambiguous references, and long-range dependencies make entity resolution in literary texts particularly challenging. |
| Approach: | They present a large-scale dataset for end-to-end Entity Discovery and Linking (EDL) in Sanskrit. |
| Outcome: | The proposed dataset is aligned with an English knowledge base to support cross-lingual linking. |