Datasets:
Tasks:
Translation
Formats:
csv
Size:
10K - 100K
ArXiv:
Tags:
machine-translation
quality-estimation
post-editing
translation
behavioral-data
multidimensional-quality-metric
License:
Link paper to HF papers URL
Browse filesThe current paper link is to arxiv.org, this PR changes it to hf.co/papers.
README.md
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---
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language:
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- en
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- it
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- nl
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license:
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- apache-2.0
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tags:
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- machine-translation
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- quality-estimation
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- mqm
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- comet
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- qe
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language_creators:
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- machine-generated
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- expert-generated
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annotations_creators:
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- machine-generated
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pretty_name: qe4pe
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size_categories:
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- 10K<n<100K
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source_datasets:
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- Unbabel/TowerEval-Data-v0.1
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task_categories:
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- translation
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configs:
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- config_name: main
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data_files:
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# Quality Estimation for Post-Editing (QE4PE)
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*For more details on QE4PE, see our [paper](
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## Dataset Description
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- **Source:** [Github](https://github.com/gsarti/qe4pe)
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- **Paper:** [Arxiv](https://
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- **Point of Contact:** [Gabriele Sarti](mailto:gabriele.sarti996@gmail.com)
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[Gabriele Sarti](https://gsarti.com) • [Vilém Zouhar](https://vilda.net/) • [Grzegorz Chrupała](https://grzegorz.chrupala.me/) • [Ana Guerberof Arenas](https://scholar.google.com/citations?user=i6bqaTsAAAAJ) • [Malvina Nissim](https://malvinanissim.github.io/) • [Arianna Bisazza](https://www.cs.rug.nl/~bisazza/)
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### News 📢
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**March 2025**: The QE4PE paper is available on [Arxiv](https://
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**January 2025**: MQM annotations are now available for the `main` task.
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|`mt_text` | Output of the `NLLB-3.3B` model when translating `src_text` into `tgt_lang` (default config, 5 beams) |
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|`mt_text_highlighted` | Highlighted version of `mt_text` with potential errors according to the `highlight_modality`. |
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|`pe_text` | Post-edited version of `mt_text` produced by a professional translator with `highlight_modality`. |
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|`mt_pe_word_aligned` | Aligned visual representation of word-level edit operations (I = Insertion, D = Deletion, S = Substitution) (replace `\
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|`highlights` | List of dictionaries for highlighted spans with error severity and position, matching XCOMET format for word-level error annotations. |
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|**MQM annotations (`main` config only)**| |
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|`qa_mt_annotator_id` | Annotator ID for the MQM evaluation of `qa_mt_annotated_text`. |
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"pe_chrf_mean": 79.173,
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"pe_chrf_std": 13.679,
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"pe_ter_max": 100.0,
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"pe_ter_min": 0.0
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"pe_ter_mean": 28.814,
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"pe_ter_std": 28.827,
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"pe_comet_max": 0.977,
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"pe_comet_min": 0.851,
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"pe_comet_mean": 0.937,
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"pe_comet_std": 0.035,
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"pe_xcomet_qe": 0.984,
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"pe_xcomet_errors": "[]",
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# Behavioral data
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"doc_num_edits": 103,
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"doc_edit_order": 20,
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"doc_edit_time": 118,
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"doc_edit_time_filtered": 118,
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"doc_keys_per_min": 52.37,
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"doc_chars_per_min": 584.24,
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"doc_words_per_min": 79.83,
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"segment_num_edits": 9,
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"segment_edit_order": 3,
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"segment_edit_time": 9,
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"segment_edit_time_filtered": 9,
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"segment_keys_per_min": 60.0,
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"segment_chars_per_min": 906.67,
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"segment_words_per_min": 106.67,
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"num_enter_actions": 2,
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"remove_highlights": False,
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# Texts and annotations
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"src_text": "The speed of its emerging growth frequently outpaces the development of quality assurance and education.",
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"mt_text": "De snelheid van de opkomende groei is vaak sneller dan de ontwikkeling van kwaliteitsborging en onderwijs.",
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"mt_text_highlighted": "De snelheid van de opkomende groei is vaak <minor>sneller</minor> dan de ontwikkeling van kwaliteitsborging en <major>onderwijs.</major>",
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"pe_text": "De snelheid van de opkomende groei is vaak sneller dan de ontwikkeling van kwaliteitsborging en opleiding.",
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"mt_pe_word_aligned": "MT: De snelheid van de opkomende groei is vaak sneller dan de ontwikkeling van kwaliteitsborging en onderwijs.\n" \
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"PE: De snelheid van de opkomende groei is vaak sneller dan de ontwikkeling van kwaliteitsborging en opleiding.\n" \
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" S",
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"mt_pe_char_aligned": "MT: De snelheid van de opkomende groei is vaak sneller dan de ontwikkeling van kwaliteitsborging en onderwijs.\n" \
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"PE: De snelheid van de opkomende groei is vaak sneller dan de ontwikkeling van kwaliteitsborging en opleiding.\n" \
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" SS SS SS ",
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"highlights": """[
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{
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'text': 'sneller',
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'severity': 'minor',
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'start': 43,
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'end': 50
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},
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{
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'text': 'onderwijs.',
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'severity': 'major',
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'start': 96,
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'end': 106
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}
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]"""
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# QA annotations
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"qa_mt_annotator_id": 'qa_nld_3',
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"qa_pe_annotator_id": 'qa_nld_1',
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"qa_mt_esa_rating": 100.0,
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"qa_pe_esa_rating": 80.0,
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"qa_mt_annotated_text": "De snelheid van de opkomende groei is vaak sneller dan de ontwikkeling van kwaliteitsborging en onderwijs.",
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"qa_pe_annotated_text": "De snelheid van de opkomende groei is vaak sneller dan de ontwikkeling van kwaliteitsborging en opleiding.",
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"qa_mt_fixed_text": "De snelheid van de opkomende groei is vaak sneller dan de ontwikkeling van kwaliteitsborging en onderwijs.",
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"qa_pe_fixed_text": "De snelheid van de ontluikende groei overtreft vaak de ontwikkeling van kwaliteitsborging en onderwijs.",
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"qa_mt_mqm_errors": "[]",
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"qa_pe_mqm_errors": """[
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{
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"text": "opkomende",
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"text_start": 19,
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"text_end": 28,
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"correction":
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"ontluikende",
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"correction_start": 19,
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"correction_end": 30,
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"description": "Mistranslation - not the correct word",
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"mqm_category": "Mistranslation",
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"severity": "Minor",
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"comment": "",
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"edit_order": 1
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}
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]"""
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}
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```
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The text is provided as-is, without further preprocessing or tokenization.
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### Dataset Creation
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The datasets were parsed from GroTE inputs, logs and outputs for the QE4PE study, available in this repository. Processed dataframes using the `qe4pe process_task_data` command. Refer to the [QE4PE Github repository](https://github.com/gsarti/qe4pe) for additional details. The overall structure and processing of the dataset were inspired by the [DivEMT dataset](https://huggingface.co/datasets/GroNLP/divemt).
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### QA Annotations
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MQM annotations were collected using Google Sheets and highlights were parsed from HTML exported output, ensuring their compliance with well-formedness checks. Out of the original 51 docs (324 segments) in `main`, 24 docs (10 biomedical, 14 social, totaling 148 segments) were samples at random and annotated by professional translators.
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## Additional Information
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### Metric signatures
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The following signatures correspond to the metrics reported in the processed dataframes:
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```shell
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# Computed using SacreBLEU: https://github.com/mjpost/sacrebleu
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BLEU: case:mixed|eff:yes|tok:13a|smooth:exp|version:2.3.1
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ChrF: case:mixed|eff:yes|nc:6|nw:0|space:no|version:2.3.1
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TER: case:lc|tok:tercom|norm:no|punct:yes|asian:no|version:2.3.1
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# Computed using Unbabel COMET: https://github.com/Unbabel/COMET
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Comet: Python3.11.9|Comet2.2.2|fp32|Unbabel/wmt22-comet-da
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XComet: Python3.10.12|Comet2.2.1|fp32|Unbabel/XCOMET-XXL
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```
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### Dataset Curators
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For problems related to this 🤗 Datasets version, please contact me at [gabriele.sarti996@gmail.com](mailto:gabriele.sarti996@gmail.com).
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### Citation Information
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```bibtex
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@misc{sarti-etal-2024-qe4pe,
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title={{QE4PE}: Word-level Quality Estimation for Human Post-Editing},
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author={Gabriele Sarti and Vilém Zouhar and Grzegorz Chrupała and Ana Guerberof-Arenas and Malvina Nissim and Arianna Bisazza},
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year={2025},
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eprint={2503.03044},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2503.03044},
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}
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```
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---
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annotations_creators:
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- machine-generated
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language_creators:
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- machine-generated
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- expert-generated
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language:
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- en
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- it
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- nl
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license:
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- apache-2.0
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size_categories:
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- 10K<n<100K
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source_datasets:
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- Unbabel/TowerEval-Data-v0.1
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task_categories:
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- translation
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pretty_name: qe4pe
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tags:
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- machine-translation
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- quality-estimation
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- mqm
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- comet
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- qe
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configs:
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- config_name: main
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data_files:
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# Quality Estimation for Post-Editing (QE4PE)
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*For more details on QE4PE, see our [paper](https://huggingface.co/papers/2503.03044) and our [Github repository](https://github.com/gsarti/qe4pe)*
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## Dataset Description
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- **Source:** [Github](https://github.com/gsarti/qe4pe)
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- **Paper:** [Arxiv](https://huggingface.co/papers/2503.03044)
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- **Point of Contact:** [Gabriele Sarti](mailto:gabriele.sarti996@gmail.com)
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[Gabriele Sarti](https://gsarti.com) • [Vilém Zouhar](https://vilda.net/) • [Grzegorz Chrupała](https://grzegorz.chrupala.me/) • [Ana Guerberof Arenas](https://scholar.google.com/citations?user=i6bqaTsAAAAJ) • [Malvina Nissim](https://malvinanissim.github.io/) • [Arianna Bisazza](https://www.cs.rug.nl/~bisazza/)
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### News 📢
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**March 2025**: The QE4PE paper is available on [Arxiv](https://huggingface.co/papers/2503.03044).
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**January 2025**: MQM annotations are now available for the `main` task.
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|`mt_text` | Output of the `NLLB-3.3B` model when translating `src_text` into `tgt_lang` (default config, 5 beams) |
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|`mt_text_highlighted` | Highlighted version of `mt_text` with potential errors according to the `highlight_modality`. |
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|`pe_text` | Post-edited version of `mt_text` produced by a professional translator with `highlight_modality`. |
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|`mt_pe_word_aligned` | Aligned visual representation of word-level edit operations (I = Insertion, D = Deletion, S = Substitution) (replace `\
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` with `
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` to show the three aligned rows). |
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|`mt_pe_char_aligned` | Aligned visual representation of character-level edit operations (I = Insertion, D = Deletion, S = Substitution) (replace `\
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` with `
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` to show the three aligned rows). |
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|`highlights` | List of dictionaries for highlighted spans with error severity and position, matching XCOMET format for word-level error annotations. |
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|**MQM annotations (`main` config only)**| |
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|`qa_mt_annotator_id` | Annotator ID for the MQM evaluation of `qa_mt_annotated_text`. |
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"pe_chrf_mean": 79.173,
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"pe_chrf_std": 13.679,
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"pe_ter_max": 100.0,
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"pe_ter_min": 0.0
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