技术讲座翻译中的挑战：来自NPTEL的洞察
Challenges in Translating Technical Lectures: Insights from the NPTEL
AI总结：
研究探讨机器翻译在印度语言中的实际应用及方法论影响，重点分析Bangla、Malayalam和Telugu语言的翻译流程与评估框架，揭示多语言教育技术在NEP 2020下的重要性。
摘要：
本研究探讨了机器翻译在印度语言（如孟加拉语、马拉雅拉姆语和泰卢固语）中的实际应用和方法论影响，特别是在新兴翻译流程和现有评估框架中的应用。所选语言的选择基于语言多样性三角分析，体现了在NEP 2020背景下多语言教育技术的重要性。这进一步得到最大的MOOC平台NPTEL的支持，其作为语料库促进了本文论点的论证。本文强调了自发口语语料库的编纂，以确保技术概念的清晰传达，同时考虑适当的语域和词汇选择，这对于像印度这样语言多样性丰富的国家至关重要。本研究的发现突显了特定度量标准的敏感性以及在表面重叠度量下测试时形态丰富且语义紧凑的特征所带来的挑战。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
