# Challenges in Translating Technical Lectures: Insights from the NPTEL

arXiv: https://arxiv.org/abs/2602.08698 · HTML: https://mlsanity.com/paper/2602.08698/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 25 references that are in the map.
- First posted on arXiv 9 Feb 2026.

## Builds on (most cited first, 8 of 8)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [COMET: A Neural Framework for MT Evaluation](https://mlsanity.com/paper/2009.09025/) (2020)
- [Neural Approaches to Conversational AI](https://mlsanity.com/paper/1809.08267/) (2018)
- [A Survey of Domain Adaptation for Neural Machine Translation](https://mlsanity.com/paper/1806.00258/) (2018)
- [Fluent Translations from Disfluent Speech in End-to-End Speech Translation](https://mlsanity.com/paper/1906.00556/) (2019)
- [Neural versus Phrase-Based Machine Translation Quality: a Case Study](https://mlsanity.com/paper/1608.04631/) (2016)
- [BhashaVerse : Translation Ecosystem for Indian Subcontinent Languages](https://mlsanity.com/paper/2412.04351/) (2024)

## Abstract (arXiv)

This study examines the practical applications and methodological implications of Machine Translation in Indian Languages, specifically Bangla, Malayalam, and Telugu, within emerging translation workflows and in relation to existing evaluation frameworks. The choice of languages prioritized in this study is motivated by a triangulation of linguistic diversity, which illustrates the significance of multilingual accommodation of educational technology under NEP 2020. This is further supported by the largest MOOC portal, i.e., NPTEL, which has served as a corpus to facilitate the arguments presented in this paper. The curation of a spontaneous speech corpora that accounts for lucid delivery of technical concepts, considering the retention of suitable register and lexical choices are crucial in a diverse country like India. The findings of this study highlight metric-specific sensitivity and the challenges of morphologically rich and semantically compact features when tested against surface overlapping metrics.