Challenges in Translating Technical Lectures: Insights from the NPTEL

TL;DR

This paper explores the challenges and methodological considerations of applying Machine Translation to Indian languages in educational contexts, using NPTEL as a key resource.

Contribution

It provides insights into the practical issues of translating technical lectures in multilingual Indian educational settings, emphasizing evaluation challenges.

Findings

Metric-specific sensitivity affects translation quality assessment.

Morphologically rich features pose challenges for current metrics.

Surface overlapping metrics may not fully capture translation adequacy.

Abstract

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.