Translating Step-by-Step: Decomposing the Translation Process for Improved Translation Quality of Long-Form Texts
Eleftheria Briakou, Jiaming Luo, Colin Cherry, Markus Freitag

TL;DR
This paper introduces a step-by-step translation framework for long-form texts that enhances translation quality by involving multi-turn interactions with language models, outperforming traditional methods.
Contribution
It proposes a novel multi-turn, step-by-step translation process inspired by translation studies, significantly improving long-form translation quality over existing approaches.
Findings
Achieved state-of-the-art results on WMT2024.
Large quality improvements over zero-shot prompting.
Effective multi-turn interaction enhances translation accuracy.
Abstract
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.
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Taxonomy
TopicsTranslation Studies and Practices · Natural Language Processing Techniques
