Translating Step-by-Step: Decomposing the Translation Process for Improved Translation Quality of Long-Form Texts
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866913496824807424 |
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| author | Briakou, Eleftheria Luo, Jiaming Cherry, Colin Freitag, Markus |
| author_facet | Briakou, Eleftheria Luo, Jiaming Cherry, Colin Freitag, Markus |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_06790 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Translating Step-by-Step: Decomposing the Translation Process for Improved Translation Quality of Long-Form Texts Briakou, Eleftheria Luo, Jiaming Cherry, Colin Freitag, Markus Computation and Language 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. |
| title | Translating Step-by-Step: Decomposing the Translation Process for Improved Translation Quality of Long-Form Texts |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2409.06790 |