# LEVOS: Leveraging Vocabulary Overlap with Sanskrit to Generate Technical Lexicons in Indian Languages

arXiv: https://arxiv.org/abs/2407.06331 · HTML: https://mlsanity.com/paper/2407.06331/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 22 references that are in the map.
- First posted on arXiv 8 Jul 2024.

## Builds on (most cited first, 9 of 9)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)
- [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://mlsanity.com/paper/2105.13626/) (2021)
- [TransLIST: A Transformer-Based Linguistically Informed Sanskrit Tokenizer](https://mlsanity.com/paper/2210.11753/) (2022)
- [Validation and Normalization of DCS corpus using Sanskrit Heritage tools to build a tagged Gold Corpus](https://mlsanity.com/paper/2005.06545/) (2020)
- [Sanskrit Sandhi Splitting using seq2(seq)^2](https://mlsanity.com/paper/1801.00428/) (2018)
- [Neural Compound-Word (Sandhi) Generation and Splitting in Sanskrit Language](https://mlsanity.com/paper/2010.12940/) (2020)
- [LexGen: Domain-aware Multilingual Lexicon Generation](https://mlsanity.com/paper/2405.11200/) (2024)
- [Faster decoding for subword level Phrase-based SMT between related languages](https://mlsanity.com/paper/1611.00354/) (2016)

## Built on by (most cited first, 4 of 4)

- [Incentives Of EdTech: A Systematic Review Of EduNLP Research](https://mlsanity.com/paper/2606.13691/) (2026)
- [A P=aninian Foundation for Indic Language Processing](https://mlsanity.com/paper/2606.24172/) (2026)
- [A Computational Approach to Measuring Semantic Change in Sanskrit Literature](https://mlsanity.com/paper/2609.25012/) (2026)
- [A Character-Level Neural Approach to Sinhala Sandhi Splitting](https://mlsanity.com/paper/2609.36131/) (2026)

## Abstract (arXiv)

Translating technical terms into lexically similar, low-resource Indian languages remains a challenge due to limited parallel data and the complexity of linguistic structures. We propose a novel use-case of Sanskrit-based segments for linguistically informed translation of such terms, leveraging subword-level similarity and morphological alignment across related languages. Our approach uses character-level segmentation to identify meaningful subword units, facilitating more accurate and context-aware translation. To enable this, we utilize a Character-level Transformer model for Sanskrit Word Segmentation (CharSS), which addresses the complexities of sandhi and morpho-phonemic changes during segmentation. We observe consistent improvements in two experimental settings for technical term translation using Sanskrit-derived segments, averaging 8.46 and 6.79 chrF++ scores, respectively. Further, we conduct a post hoc human evaluation to verify the quality assessment of the translated technical terms using automated metrics. This work has important implications for the education field, especially in creating accessible, high-quality learning materials in Indian languages. By supporting the accurate and linguistically rooted translation of technical content, our approach facilitates inclusivity and aids in bridging the resource gap for learners in low-resource language communities.