LATA：一个辅助翻译标注的工具
LATA: A Tool for LLM-Assisted Translation Annotation
AI总结：
LATA是一个利用大语言模型辅助的翻译标注工具，旨在提高标注效率的同时保持语言学精度，适用于结构差异大的语言对。
摘要：
高质量平行语料库的构建对于翻译研究来说，已从简单的句子对齐逐渐发展为复杂的、多层标注任务。这种方法论的转变给结构差异较大的语言对，如阿拉伯语-英语，带来了巨大挑战，其中标准自动工具经常无法捕捉到深层次的语言变化或语义细微差别。本文介绍了一种新颖的LLM辅助交互工具，旨在缩小可扩展自动化与专家人类判断所需严谨精度之间的差距。与传统统计对齐器不同，我们的系统采用基于模板的提示管理器，利用大语言模型（LLMs）在严格JSON输出约束下进行句子分割和对齐。在该工具中，自动预处理整合到人类在环的工作流程中，使研究者能够精修对齐并应用自定义翻译技术标注通过站位架构。通过利用LLM辅助处理，该工具在标注效率与分析复杂翻译现象所需语言学精度之间取得了平衡。
英文摘要：
The construction of high-quality parallel corpora for translation research has increasingly evolved from simple sentence alignment to complex, multi-layered annotation tasks. This methodological shift presents significant challenges for structurally divergent language pairs, such as Arabic--English, where standard automated tools frequently fail to capture deep linguistic shifts or semantic nuances. This paper introduces a novel, LLM-assisted interactive tool designed to reduce the gap between scalable automation and the rigorous precision required for expert human judgment. Unlike traditional statistical aligners, our system employs a template-based Prompt Manager that leverages large language models (LLMs) for sentence segmentation and alignment under strict JSON output constraints. In this tool, automated preprocessing integrates into a human-in-the-loop workflow, allowing researchers to refine alignments and apply custom translation technique annotations through a stand-off architecture. By leveraging LLM-assisted processing, the tool balances annotation efficiency with the linguistic precision required to analyze complex translation phenomena in specialized domains.
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。
