README.md

metadata

license: cc-by-sa-4.0
dataset_info:
  - config_name: de-enGB
    features:
      - name: source
        dtype: large_string
      - name: translation_A
        dtype: large_string
      - name: translation_B
        dtype: large_string
      - name: A
        dtype: bool
      - name: equal
        dtype: bool
      - name: B
        dtype: bool
      - name: label_A
        dtype: large_string
      - name: label_B
        dtype: large_string
      - name: text
        dtype: large_string
      - name: text_type
        dtype: large_string
    splits:
      - name: train
        num_bytes: 99615
        num_examples: 390
    download_size: 51640
    dataset_size: 99615
  - config_name: de-frCH
    features:
      - name: source
        dtype: large_string
      - name: translation_A
        dtype: large_string
      - name: translation_B
        dtype: large_string
      - name: A
        dtype: bool
      - name: equal
        dtype: bool
      - name: B
        dtype: bool
      - name: label_A
        dtype: large_string
      - name: label_B
        dtype: large_string
      - name: text
        dtype: large_string
      - name: text_type
        dtype: large_string
    splits:
      - name: train
        num_bytes: 106345
        num_examples: 385
    download_size: 55015
    dataset_size: 106345
  - config_name: de-itCH
    features:
      - name: source
        dtype: large_string
      - name: translation_A
        dtype: large_string
      - name: translation_B
        dtype: large_string
      - name: A
        dtype: bool
      - name: equal
        dtype: bool
      - name: B
        dtype: bool
      - name: label_A
        dtype: large_string
      - name: label_B
        dtype: large_string
      - name: text
        dtype: large_string
      - name: text_type
        dtype: large_string
    splits:
      - name: train
        num_bytes: 102833
        num_examples: 378
    download_size: 54128
    dataset_size: 102833
  - config_name: en-deCH
    features:
      - name: source
        dtype: large_string
      - name: translation_A
        dtype: large_string
      - name: translation_B
        dtype: large_string
      - name: A
        dtype: bool
      - name: equal
        dtype: bool
      - name: B
        dtype: bool
      - name: label_A
        dtype: large_string
      - name: label_B
        dtype: large_string
      - name: text
        dtype: large_string
      - name: text_type
        dtype: large_string
    splits:
      - name: train
        num_bytes: 99510
        num_examples: 330
    download_size: 49779
    dataset_size: 99510
configs:
  - config_name: de-enGB
    data_files:
      - split: train
        path: de-enGB/train-*
  - config_name: de-frCH
    data_files:
      - split: train
        path: de-frCH/train-*
  - config_name: de-itCH
    data_files:
      - split: train
        path: de-itCH/train-*
  - config_name: en-deCH
    data_files:
      - split: train
        path: en-deCH/train-*
task_categories:
  - translation
language:
  - en
  - de
  - it
  - fr
tags:
  - Supertext
  - DeepL
  - Translation
  - A/B-test
pretty_name: A/B Test Supertext vs DeepL
size_categories:
  - 1K<n<10K

A/B Test Supertext vs DeepL

We release all evaluation data and scripts for further analysis and reproduction of the accompanying paper: A comparison of translation performance between DeepL and Supertext. The data consists of document-level translations by Supertext and DeepL as well as accompanying ratings by professional translators. Please find more details in the paper.

Please note that the empty lines correspond to paragraph boundaries (i.e., double line breaks) in the original documents.

# for each language pair, there is a separate subset
data = load_dataset("Supertext/mt-doclevel-ab-test", "en-deCH")

Dataset Details

As strong machine translation (MT) systems are increasingly based on large language models (LLMs), reliable quality benchmarking requires methods that capture their ability to leverage extended context. This study compares two commercial MT systems -- DeepL and Supertext -- by assessing their performance on unsegmented texts. We evaluate translation quality across four language directions with professional translators assessing segments with full document-level context. While segment-level assessments indicate no strong preference between the systems in most cases, document-level analysis reveals a preference for Supertext in three out of four language directions, suggesting superior consistency across longer texts. We advocate for more context-sensitive evaluation methodologies to ensure that MT quality assessments reflect real-world usability.

Citation

If you use any of the data released in our work, please cite the following paper:

@misc{flückiger2025comparisontranslationperformancedeepl,
      title={A comparison of translation performance between DeepL and Supertext}, 
      author={Alex Flückiger and Chantal Amrhein and Tim Graf and Frédéric Odermatt and Martin Pömsl and Philippe Schläpfer and Florian Schottmann and Samuel Läubli},
      year={2025},
      eprint={2502.02577},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2502.02577}, 
}

Dataset Description

• Curated by: Supertext
• Language(s) (NLP): English, French, German, Italian
• License: CC BY-SA 4.0

Dataset Sources

• Repository: https://github.com/Supertext/evaluation_deepl_supertext
• Paper: https://arxiv.org/abs/2502.02577