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.
@article{arxiv.2409.06790,
title = {Translating Step-by-Step: Decomposing the Translation Process for Improved Translation Quality of Long-Form Texts},
author = {Eleftheria Briakou and Jiaming Luo and Colin Cherry and Markus Freitag},
journal= {arXiv preprint arXiv:2409.06790},
year = {2024}
}