# SanskritShala: A Neural Sanskrit NLP Toolkit with Web-Based Interface for Pedagogical and Annotation Purposes

arXiv: https://arxiv.org/abs/2302.09527 · HTML: https://mlsanity.com/paper/2302.09527/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 41 references that are in the map.
- First posted on arXiv 19 Feb 2023.

## Builds on (most cited first, 21 of 21)

- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Exploring the Limits of Language Modeling](https://mlsanity.com/paper/1602.02410/) (2016)
- [Character-Aware Neural Language Models](https://mlsanity.com/paper/1508.06615/) (2015)
- [BPEmb: Tokenization-free Pre-trained Subword Embeddings in 275 Languages](https://mlsanity.com/paper/1710.02187/) (2017)
- [Charagram: Embedding Words and Sentences via Character n-grams](https://mlsanity.com/paper/1607.02789/) (2016)
- [Automatic Speech Recognition in Sanskrit: A New Speech Corpus and Modelling Insights](https://mlsanity.com/paper/2106.05852/) (2021)
- [TransLIST: A Transformer-Based Linguistically Informed Sanskrit Tokenizer](https://mlsanity.com/paper/2210.11753/) (2022)
- [A Little Pretraining Goes a Long Way: A Case Study on Dependency Parsing Task for Low-resource Morphologically Rich Languages](https://mlsanity.com/paper/2102.06551/) (2021)
- [LemmaTag: Jointly Tagging and Lemmatizing for Morphologically-Rich Languages with BRNNs](https://mlsanity.com/paper/1808.03703/) (2018)
- [Free as in Free Word Order: An Energy Based Model for Word Segmentation and Morphological Tagging in Sanskrit](https://mlsanity.com/paper/1809.01446/) (2018)
- [Evaluating Neural Word Embeddings for Sanskrit](https://mlsanity.com/paper/2104.00270/) (2021)
- [Evaluating Neural Morphological Taggers for Sanskrit](https://mlsanity.com/paper/2005.10893/) (2020)
- [Validation and Normalization of DCS corpus using Sanskrit Heritage tools to build a tagged Gold Corpus](https://mlsanity.com/paper/2005.06545/) (2020)
- [Neural Approaches for Data Driven Dependency Parsing in Sanskrit](https://mlsanity.com/paper/2004.08076/) (2020)
- [Sangrahaka: A Tool for Annotating and Querying Knowledge Graphs](https://mlsanity.com/paper/2107.02782/) (2021)
- [A Novel Multi-Task Learning Approach for Context-Sensitive Compound Type Identification in Sanskrit](https://mlsanity.com/paper/2208.10310/) (2022)
- [Chandojnanam: A Sanskrit Meter Identification and Utilization System](https://mlsanity.com/paper/2209.14924/) (2022)
- [Prabhupadavani: A Code-mixed Speech Translation Data for 25 Languages](https://mlsanity.com/paper/2201.11391/) (2022)

## Built on by (most cited first, 5 of 5)

- [HERITAGE: An End-to-End Web Platform for Processing Korean Historical Documents in Hanja](https://mlsanity.com/paper/2501.11951/) (2025)
- [Still Not There: Can LLMs Outperform Smaller Task-Specific Seq2Seq Models on the Poetry-to-Prose Conversion Task?](https://mlsanity.com/paper/2511.08145/) (2025)
- [BHARATI: Morphology-Aware Tokenizers for Classical Indian Languages with Subword Fertility Analysis](https://mlsanity.com/paper/2607.23319/) (2026)
- [Vectorizing Classical Tamil: Representation Learning for Verse-Commentary Pairs](https://mlsanity.com/paper/2609.04755/) (2026)
- [A Computational Approach to Measuring Semantic Change in Sanskrit Literature](https://mlsanity.com/paper/2609.25012/) (2026)

## Abstract (arXiv)

We present a neural Sanskrit Natural Language Processing (NLP) toolkit named SanskritShala (a school of Sanskrit) to facilitate computational linguistic analyses for several tasks such as word segmentation, morphological tagging, dependency parsing, and compound type identification. Our systems currently report state-of-the-art performance on available benchmark datasets for all tasks. SanskritShala is deployed as a web-based application, which allows a user to get real-time analysis for the given input. It is built with easy-to-use interactive data annotation features that allow annotators to correct the system predictions when it makes mistakes. We publicly release the source codes of the 4 modules included in the toolkit, 7 word embedding models that have been trained on publicly available Sanskrit corpora and multiple annotated datasets such as word similarity, relatedness, categorization, analogy prediction to assess intrinsic properties of word embeddings. So far as we know, this is the first neural-based Sanskrit NLP toolkit that has a web-based interface and a number of NLP modules. We are sure that the people who are willing to work with Sanskrit will find it useful for pedagogical and annotative purposes. SanskritShala is available at: https://cnerg.iitkgp.ac.in/sanskritshala. The demo video of our platform can be accessed at: https://youtu.be/x0X31Y9k0mw4.