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LangMark: A Multilingual Dataset for Automatic Post-Editing

arXiv 2511.17153 ↗First posted 21 Nov 2025cs.CLcited by 0

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Abstract (arXiv)

Automatic post-editing (APE) aims to correct errors in machine-translated text, enhancing translation quality, while reducing the need for human intervention. Despite advances in neural machine translation (NMT), the development of effective APE systems has been hindered by the lack of large-scale multilingual datasets specifically tailored to NMT outputs. To address this gap, we present and release LangMark, a new human-annotated multilingual APE dataset for English translation to seven languages: Brazilian Portuguese, French, German, Italian, Japanese, Russian, and Spanish. The dataset has 206,983 triplets, with each triplet consisting of a source segment, its NMT output, and a human post-edited translation. Annotated by expert human linguists, our dataset offers both linguistic diversity and scale. Leveraging this dataset, we empirically show that Large Language Models (LLMs) with few-shot prompting can effectively perform APE, improving upon leading commercial and even proprietary machine translation systems. We believe that this new resource will facilitate the future development and evaluation of APE systems.

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References · 17

  1. 2017Attention Is All You Needcited by 59,932
  2. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  3. 2023DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelinescited by 803
  4. 2022Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLPcited by 316
  5. 2022What Language Model Architecture and Pretraining Objective Work Best for Zero-Shot Generalization?cited by 149
  6. 2023Leveraging GPT-4 for Automatic Translation Post-Editingcited by 56
  7. 2019Context-Aware Monolingual Repair for Neural Machine Translationcited by 41
  8. 2024Translating Step-by-Step: Decomposing the Translation Process for Improved Translation Quality of Long-Form Textscited by 31
  9. 2024Guiding Large Language Models to Post-Edit Machine Translation with Error Annotationscited by 22
  10. 2023Contextual Refinement of Translations: Large Language Models for Sentence and Document-Level Post-Editingcited by 21
  11. 2020Can Automatic Post-Editing Improve NMT?cited by 17
  12. 2024xTower: A Multilingual LLM for Explaining and Correcting Translation Errorscited by 17
5 more
  1. 2016Log-linear Combinations of Monolingual and Bilingual Neural Machine Translation Models for Automatic Post-Editingcited by 13
  2. 2018MS-UEdin Submission to the WMT2018 APE Shared Task: Dual-Source Transformer for Automatic Post-Editingcited by 13
  3. 2019A Simple and Effective Approach to Automatic Post-Editing with Transfer Learningcited by 11
  4. 2019Automatic Post-Editing for Machine Translationcited by 8
  5. 2020Learning Non-Monotonic Automatic Post-Editing of Translations from Human Orderingscited by 6

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