# Sanskrit Sandhi Splitting using seq2(seq)^2

arXiv: https://arxiv.org/abs/1801.00428 · HTML: https://mlsanity.com/paper/1801.00428/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jan 2018.

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

- [Itihasa: A large-scale corpus for Sanskrit to English translation](https://mlsanity.com/paper/2106.03269/) (2021)
- [TransLIST: A Transformer-Based Linguistically Informed Sanskrit Tokenizer](https://mlsanity.com/paper/2210.11753/) (2022)
- [LEVOS: Leveraging Vocabulary Overlap with Sanskrit to Generate Technical Lexicons in Indian Languages](https://mlsanity.com/paper/2407.06331/) (2024)
- [Mapping Hymns and Organizing Concepts in the Rigveda: Quantitatively Connecting the Vedic Suktas](https://mlsanity.com/paper/2503.18226/) (2025)
- [A Character-Level Neural Approach to Sinhala Sandhi Splitting](https://mlsanity.com/paper/2609.36131/) (2026)

## Abstract (arXiv)

In Sanskrit, small words (morphemes) are combined to form compound words through a process known as Sandhi. Sandhi splitting is the process of splitting a given compound word into its constituent morphemes. Although rules governing word splitting exists in the language, it is highly challenging to identify the location of the splits in a compound word. Though existing Sandhi splitting systems incorporate these pre-defined splitting rules, they have a low accuracy as the same compound word might be broken down in multiple ways to provide syntactically correct splits. In this research, we propose a novel deep learning architecture called Double Decoder RNN (DD-RNN), which (i) predicts the location of the split(s) with 95% accuracy, and (ii) predicts the constituent words (learning the Sandhi splitting rules) with 79.5% accuracy, outperforming the state-of-art by 20%. Additionally, we show the generalization capability of our deep learning model, by showing competitive results in the problem of Chinese word segmentation, as well.