解决不可能之事：香港判例法的翻译
Solving the Unsolvable: Translating Case Law in Hong Kong
- UOW College Hong Kong（香港UOW学院）
- City University of Hong Kong（香港城市大学）
- The Chinese University of Hong Kong Shenzhen（香港中文大学深圳校区）
- The University of Hong Kong（香港大学）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
本文针对香港判例法翻译的挑战，提出利用人机交互翻译平台，结合大型语言模型和多智能体系统，以实现高效高质量的司法判决翻译。
中文摘要 AI 辅助
本文探讨了在香港双语法律体系下翻译判例法所面临的挑战。文章强调了1997年移交前将所有成文法翻译成中文的初步成功，这是《基本法》规定的一项任务。这项工作涉及法律、语言和翻译专家之间的重大合作，最终形成了一个全面且文化上恰当的双语法律体系。然而，由于司法判决数量庞大且持续增长，翻译判例法仍然是一个重大挑战。本文批评了政府和司法机构在翻译判例法方面零星且不协调的努力，并将其与之前成文法翻译所采取的彻底方法进行对比。尽管政府承认法律双语的重要性，但缺乏翻译判例法的可持续战略。司法机构认为翻译所有判决是不必要、不现实且不具成本效益的立场，本文对此进行了分析并批评其对法律透明度和公众信任的影响。提出的解决方案涉及通过人机交互翻译平台利用机器翻译技术，该平台经历两次重大转变。最初基于神经模型，平台转向使用大型语言模型以提高翻译准确性。此外，它从单智能体系统演变为多智能体系统，包含翻译员、注释员和校对员智能体。这种多智能体方法在资助支持下，旨在通过整合先进人工智能和持续反馈机制，促进司法判决的高效、高质量翻译，从而更好地满足双语法律体系的需求。
英文摘要
This paper addresses the challenges translating case law under Hong Kong's bilingual legal system. It highlights the initial success of translating all written statutes into Chinese before the 1997 handover, a task mandated by the Basic Law. The effort involved significant collaboration among legal, linguistic, and translation experts, resulting in a comprehensive and culturally appropriate bilingual legal system. However, translating case law remains a significant challenge due to the sheer volume and continuous growth of judicial decisions. The paper critiques the governments and judiciarys sporadic and uncoordinated efforts to translate case law, contrasting it with the thorough approach previously taken for statute translation. Although the government acknowledges the importance of legal bilingualism, it lacks a sustainable strategy for translating case law. The Judiciarys position that translating all judgments is unnecessary, unrealistic, and not cost-effectiveis analyzed and critiqued for its impact on legal transparency and public trust. A proposed solution involves leveraging machine translation technology through a human-machine interactive translation platform, which undergoes two major transitions. Initially based on a neural model, the platform transitions to using a large language model for improved translation accuracy. Furthermore, it evolves from a single-agent system to a multi-agent system, incorporating Translator, Annotator, and Proofreader agents. This multi-agent approach, supported by a grant, aims to facilitate efficient, high-quality translation of judicial judgments by integrating advanced artificial intelligence and continuous feedback mechanisms, thus better meeting the needs of a bilingual legal system.
