时间跨度中的脚本：翻译学在自然语言处理中演变角色的综述
Scripts Through Time: A Survey of the Evolving Role of Transliteration in NLP
AI总结：
本文综述了翻译学在跨语言NLP中的应用，分析了其在语言模型中的演变和有效性，讨论了关键权衡，并提供了针对不同语言、任务和资源约束的翻译策略建议。
摘要：
跨语言迁移在NLP中常受制于'脚本障碍'，即书写系统差异阻碍了语言间的迁移学习。翻译学，即转换脚本的过程，已成为一种强大的技术，通过增加词汇重叠来弥合这一差距。本文提供了翻译学在跨语言NLP中的广泛应用综述。我们提出了使用翻译学在语言模型中关键动机的分类，并概述了将翻译学作为输入的不同方法。我们分析了这些方法的演变和有效性，讨论了其中的关键权衡，并在现代LLMs中 contextualize 它们的必要性。本文探讨了各种设置，展示了翻译学在处理混合文本、利用语言家族相关性以及推理效率中的实际收益。基于此分析，我们为研究人员提供了具体的建议，以选择和实施最合适的翻译策略，以适应其特定的语言、任务和资源约束。
英文摘要：
Cross-lingual transfer in NLP is often hindered by the ``script barrier'' where differences in writing systems inhibit transfer learning between languages. Transliteration, the process of converting the script, has emerged as a powerful technique to bridge this gap by increasing lexical overlap. This paper provides a comprehensive survey of the application of transliteration in cross-lingual NLP. We present a taxonomy of key motivations to utilize transliterations in language models, and provide an overview of different approaches of incorporating transliterations as input. We analyze the evolution and effectiveness of these methods, discussing the critical trade-offs involved, and contextualize their need in modern LLMs. The review explores various settings that show how transliteration is beneficial, including handling code-mixed text, leveraging language family relatedness, and pragmatic gains in inference efficiency. Based on this analysis, we provide concrete recommendations for researchers on selecting and implementing the most appropriate transliteration strategy based on their specific language, task, and resource constraints.
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。
