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Translating Step-by-Step: Decomposing the Translation Process for Improved Translation Quality of Long-Form Texts

Summary

LLM translation is usually framed as one prompt that maps source text to target text. Briakou and colleagues at Google instead take the multi-stage workflow described in translation studies and turn it into a chain of prompts for Gemini 1.5 Pro. The pipeline has four stages:

  1. Research: the model lists source phrases that resist literal rendering.
  2. Drafting: it writes a draft focused on adequacy.
  3. Refinement: it rewrites the draft for fluency.
  4. Proofreading: it checks the text in a fresh conversation.

The pipeline uses only the model's parametric knowledge, with no dictionaries, retrieval or QE feedback. The prompts were tuned on a separate development set. Experiments use whole multi-segment documents from the WMT 2023 and WMT 2024 general MT tasks, translating from English into ten languages, including Ukrainian. Under reference-based and QE-based MetricX-23, the staged pipeline beats zero-shot document translation in every language and test set. It also outperforms earlier human-inspired prompting methods and reaches state-of-the-art results on WMT 2024. The work shows that structuring the process, rather than retraining the model, can raise LLM translation quality.

Key points

  • Languages: the ten out-of-English targets are ZH, UK, RU, JA, HE, CS, DE, HI, IS and ES. Evaluation relies only on the MetricX-23 family, because other neural metrics have context-window limits on long texts.
  • WMT 2024 data: documents cover four domains: Literary (40 docs, avg. 192), News (43, 184), Social (48, 164) and Speech (111, 73).
  • Design lessons:
    • Moving from segments to full documents made the research step more useful, since context resolved apparent difficulties.
    • Continuing one conversation across steps helped every stage except proofreading, which works better in a new session.
  • Comparison with prior work: unlike Wu et al. (2024), which uses 30 interacting LLM roles, this method needs just four steps and no external resources.
  • Limitations:
    • There is no human evaluation.
    • Tests were run only on Gemini.
    • The fluency-oriented refinement can sometimes regress or hallucinate content absent from the source.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Linguistics & languages · Machine translation

Cited by

34 papers of our library cite it (of the 140673 whose bibliographies we read); 4 of them have a page here, 30 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2024.wmt-1.123

@inproceedings{briakou2024translating,
  title = {{Translating Step-by-Step: Decomposing the Translation Process for Improved Translation Quality of Long-Form Texts}},
  author = {Briakou, Eleftheria and Luo, Jiaming and Cherry, Colin and Freitag, Markus},
  booktitle = {Proceedings of the Ninth Conference on Machine Translation},
  year = {2024},
  doi = {10.18653/v1/2024.wmt-1.123},
  url = {https://doi.org/10.18653/v1/2024.wmt-1.123}
}

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