逐步翻译：分解翻译过程以提升长文本翻译质量
Translating Step-by-Step: Decomposing the Translation Process for Improved Translation Quality of Long-Form Texts
- Google（谷歌公司）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究借鉴翻译研究流程，提出包含译前研究、起草等多环节的逐步翻译框架，通过语言模型多轮交互提升长文本翻译质量，经Gemini 1.5 Pro在十组语言对测试，效果优于传统方法并在WMT2024达SOTA。
AI中文摘要：
本文借鉴翻译研究中的成熟流程，提出了一种长文本翻译的逐步方法。我们并未将机器翻译视为单一的整体性任务，而是提出了一个让语言模型参与多轮交互的框架，涵盖译前研究、起草、润色与校对环节，实现翻译质量的逐步提升。使用Gemini 1.5 Pro在十个语言对上开展的大量自动评估表明，逐步翻译相比传统的零样本提示方法及早期的类人基线策略，能带来大幅的翻译质量提升，在WMT2024上取得了当前最优结果。
英文摘要：
In this paper we present a step-by-step approach to long-form text translation, drawing on established processes in translation studies. Instead of viewing machine translation as a single, monolithic task, we propose a framework that engages language models in a multi-turn interaction, encompassing pre-translation research, drafting, refining, and proofreading, resulting in progressively improved translations. Extensive automatic evaluations using Gemini 1.5 Pro across ten language pairs show that translating step-by-step yields large translation quality improvements over conventional zero-shot prompting approaches and earlier human-like baseline strategies, resulting in state-of-the-art results on WMT2024.
