# Evaluating Neural Morphological Taggers for Sanskrit

arXiv: https://arxiv.org/abs/2005.10893 · HTML: https://mlsanity.com/paper/2005.10893/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 May 2020.

## Built on by (most cited first, 7 of 7)

- [One Model is All You Need: ByT5-Sanskrit, a Unified Model for Sanskrit NLP Tasks](https://mlsanity.com/paper/2409.13920/) (2024)
- [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)
- [Evaluating Neural Word Embeddings for Sanskrit](https://mlsanity.com/paper/2104.00270/) (2021)
- [SanskritShala: A Neural Sanskrit NLP Toolkit with Web-Based Interface for Pedagogical and Annotation Purposes](https://mlsanity.com/paper/2302.09527/) (2023)
- [Morpheme Boundary Detection & Grammatical Feature Prediction for Gujarati : Dataset & Model](https://mlsanity.com/paper/2112.09860/) (2021)
- [Word Segmentation and Morphological Parsing for Sanskrit](https://mlsanity.com/paper/2201.12833/) (2022)

## Abstract (arXiv)

Neural sequence labelling approaches have achieved state of the art results in morphological tagging. We evaluate the efficacy of four standard sequence labelling models on Sanskrit, a morphologically rich, fusional Indian language. As its label space can theoretically contain more than 40,000 labels, systems that explicitly model the internal structure of a label are more suited for the task, because of their ability to generalise to labels not seen during training. We find that although some neural models perform better than others, one of the common causes for error for all of these models is mispredictions due to syncretism.