LEVOS：利用梵语词汇重叠生成印度语言技术词典
LEVOS: Leveraging Vocabulary Overlap with Sanskrit to Generate Technical Lexicons in Indian Languages
AI总结：
本文提出利用梵语段落生成印度语言技术词典的方法，通过子词级相似性和形态对齐提升翻译准确性，实验显示在技术术语翻译中取得显著提升。
摘要：
将技术术语翻译成资源有限的印度语言仍面临挑战，由于平行数据有限和语言结构复杂。我们提出利用梵语为基础的段落进行语言驱动翻译的新方法，利用子词级相似性和跨相关语言的形态对齐。我们的方法通过字符级分段识别有意义的子词单元，从而实现更准确和上下文感知的翻译。为此，我们使用字符级变换器模型进行梵语单词分段（CharSS），解决沙迪和形态语音变化的复杂性。我们观察到在两个实验设置中使用梵语衍生段落进行技术术语翻译时，平均获得了8.46和6.79的chrF++分数。进一步，我们进行事后人工评估以验证翻译技术术语的质量评估。这项工作对教育领域有重要意义，特别是在创建印度语言的可访问和高质量学习材料。通过支持技术内容的准确和语言根深蒂固的翻译，我们的方法促进了包容性，并帮助弥合低资源语言社区学习者的资源差距。
英文摘要：
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.
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。
