Datasets:
Tasks:
Translation
Formats:
csv
Size:
10K - 100K
ArXiv:
Tags:
machine-translation
quality-estimation
post-editing
translation
behavioral-data
multidimensional-quality-metric
License:
Update README.md
Browse files
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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- post-editing
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- translation
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- behavioral-data
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- multidimensional-quality-metric
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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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- split: train
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path: task/main/processed_main.csv
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- config_name: pretask
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data_files:
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- split: train
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path: task/pretask/processed_pretask.csv
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- config_name: posttask
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data_files:
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- split: train
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path: task/posttask/processed_posttask.csv
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- config_name: pretask_questionnaire
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data_files:
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- split: train
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path: questionnaires/pretask_results.csv
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- config_name: posttask_highlight_questionnaire
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data_files:
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- split: train
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path: questionnaires/posttask_highlight_results.csv
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- config_name: posttask_no_highlight_questionnaire
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data_files:
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- split: train
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path: questionnaires/posttask_no_highlight_results.csv
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---
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# Quality Estimation for Post-Editing (QE4PE)
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*For more details on QE4PE, see our [paper](TBD) 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](
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- **Point of Contact:** [Gabriele Sarti](mailto:[email protected])
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[Gabriele Sarti](https://gsarti.com) • [Vilém Zouhar](https://vilda.net/) • [
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<
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**
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The
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├──
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│ ├──
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│ ├──
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│
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├──
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│ ├── ...
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│ ├── processed_main.csv
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│ └── README.md
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### Data
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```
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| 1 |
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---
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| 2 |
+
language:
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| 3 |
+
- en
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| 4 |
+
- it
|
| 5 |
+
- nl
|
| 6 |
+
license:
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+
- apache-2.0
|
| 8 |
+
tags:
|
| 9 |
+
- machine-translation
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| 10 |
+
- quality-estimation
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| 11 |
+
- post-editing
|
| 12 |
+
- translation
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+
- behavioral-data
|
| 14 |
+
- multidimensional-quality-metric
|
| 15 |
+
- mqm
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| 16 |
+
- comet
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| 17 |
+
- qe
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| 18 |
+
language_creators:
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+
- machine-generated
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| 20 |
+
- expert-generated
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| 21 |
+
annotations_creators:
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| 22 |
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- machine-generated
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+
pretty_name: qe4pe
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| 24 |
+
size_categories:
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| 25 |
+
- 10K<n<100K
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| 26 |
+
source_datasets:
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| 27 |
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- Unbabel/TowerEval-Data-v0.1
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| 28 |
+
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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- split: train
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path: task/main/processed_main.csv
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- config_name: pretask
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data_files:
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- split: train
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path: task/pretask/processed_pretask.csv
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- config_name: posttask
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data_files:
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- split: train
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path: task/posttask/processed_posttask.csv
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- config_name: pretask_questionnaire
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data_files:
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- split: train
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path: questionnaires/pretask_results.csv
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- config_name: posttask_highlight_questionnaire
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data_files:
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- split: train
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path: questionnaires/posttask_highlight_results.csv
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- config_name: posttask_no_highlight_questionnaire
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data_files:
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- split: train
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| 54 |
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path: questionnaires/posttask_no_highlight_results.csv
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| 55 |
+
---
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| 56 |
+
|
| 57 |
+
# Quality Estimation for Post-Editing (QE4PE)
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| 58 |
+
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| 59 |
+
*For more details on QE4PE, see our [paper](TBD) and our [Github repository](https://github.com/gsarti/qe4pe)*
|
| 60 |
+
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| 61 |
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## Dataset Description
|
| 62 |
+
- **Source:** [Github](https://github.com/gsarti/qe4pe)
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| 63 |
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- **Paper:** [Arxiv](https://arxiv.org/abs/2503.03044)
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| 64 |
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- **Point of Contact:** [Gabriele Sarti](mailto:[email protected])
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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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<p float="left">
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<img src="https://github.com/gsarti/qe4pe/blob/main/figures/highlevel_qe4pe.png?raw=true" alt="QE4PE annotation pipeline" width=400/>
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</p>
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>Word-level quality estimation (QE) detects erroneous spans in machine translations, which can direct and facilitate human post-editing. While the accuracy of word-level QE systems has been assessed extensively, their usability and downstream influence on the speed, quality and editing choices of human post-editing remain understudied. Our QE4PE study investigates the impact of word-level QE on machine translation (MT) post-editing in a realistic setting involving 42 professional post-editors across two translation directions. We compare four error-span highlight modalities, including supervised and uncertainty-based word-level QE methods, for identifying potential errors in the outputs of a state-of-the-art neural MT model. Post-editing effort and productivity are estimated by behavioral logs, while quality improvements are assessed by word- and segment-level human annotation. We find that domain, language and editors' speed are critical factors in determining highlights' effectiveness, with modest differences between human-made and automated QE highlights underlining a gap between accuracy and usability in professional workflows.
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+
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+
### Dataset Summary
|
| 76 |
+
|
| 77 |
+
This dataset provides a convenient access to the processed `pretask`, `main` and `posttask` splits and the questionnaires for the QE4PE study. A sample of challenging documents extracted from WMT23 evaluation data were machine translated from English to Italian and Dutch using [NLLB 3.3B](https://huggingface.co/facebook/nllb-200-3.3B), and post-edited by 12 translators per direction across 4 highlighting modalities employing various word-level quality estimation (QE) strategies to present translators with potential errors during the editing. Additional details are provided in the [main task readme](./task/main/README.md) and in our paper. During the post-editing, behavioral data (keystrokes, pauses and editing times) were collected using the [GroTE](https://github.com/gsarti/grote) online platform. For the main task, a subset of the data was annotated with Multidimensional Quality Metrics (MQM) by professional annotators.
|
| 78 |
+
|
| 79 |
+
We publicly release the granular editing logs alongside the processed dataset to foster new research on the usability of word-level QE strategies in modern post-editing workflows.
|
| 80 |
+
|
| 81 |
+
### News 📢
|
| 82 |
+
|
| 83 |
+
**March 2025**: The QE4PE paper is available on [Arxiv](https://arxiv.org/abs/2503.03044).
|
| 84 |
+
|
| 85 |
+
**January 2025**: MQM annotations are now available for the `main` task.
|
| 86 |
+
|
| 87 |
+
**October 2024**: The QE4PE dataset is released on the HuggingFace Hub! 🎉
|
| 88 |
+
|
| 89 |
+
### Repository Structure
|
| 90 |
+
|
| 91 |
+
The repository is organized as follows:
|
| 92 |
+
|
| 93 |
+
```shell
|
| 94 |
+
qe4pe/
|
| 95 |
+
├── questionnaires/ # Configs and results for pre- and post-task questionnaires for translators
|
| 96 |
+
│ ├── pretask_results.csv # Results of the pretask questionnaire, corresponding to the `pretask_questionnaire` configuration
|
| 97 |
+
│ ├── posttask_highlight_results.csv # Results of the posttask questionnaire for highlighted modalities, corresponding to the `posttask_highlight_questionnaire` configuration
|
| 98 |
+
│ ├── posttask_no_highlight_results.csv # Results of the posttask questionnaire for the `no_highlight` modality, corresponding to the `posttask_no_highlight_questionnaire` configuration
|
| 99 |
+
│ └── ... # Configurations reporting the exact questionnaires questions and options.
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| 100 |
+
├── setup/
|
| 101 |
+
│ ├── highlights/ # Outputs of word-level QE strategies used to setup highlighted spans in the tasks
|
| 102 |
+
│ ├── qa/ # MQM/ESA annotations for the main task
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| 103 |
+
│ ├── processed/ # Intermediate outputs of the selection process for the main task
|
| 104 |
+
│ └── wmt23/ # Original collection of WMT23 sources and machine-translated outputs
|
| 105 |
+
└── task/
|
| 106 |
+
├── example/ # Example folder with task structure
|
| 107 |
+
├── main/ # Main task data, logs, outputs and guidelines
|
| 108 |
+
│ ├── ...
|
| 109 |
+
│ ├── processed_main.csv # Processed main task data, corresponds to the `main` configuration
|
| 110 |
+
│ └── README.md # Details about the main task
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| 111 |
+
├── posttask/ # Posttask task data, logs, outputs and guidelines
|
| 112 |
+
│ ├── ...
|
| 113 |
+
│ ├── processed_main.csv # Processed posttask task data, corresponds to the `posttask` configuration
|
| 114 |
+
│ └── README.md # Details about the post-task
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| 115 |
+
└── pretask/ # Pretask data, logs, outputs and guidelines
|
| 116 |
+
├── ...
|
| 117 |
+
├── processed_pretask.csv # Processed pretask data, corresponds to the `pretask` configuration
|
| 118 |
+
└── README.md # Details about the pretask
|
| 119 |
+
```
|
| 120 |
+
|
| 121 |
+
### Languages
|
| 122 |
+
|
| 123 |
+
The language data of QE4PE is in English (BCP-47 `en`), Italian (BCP-47 `it`) and Dutch (BCP-47 `nl`).
|
| 124 |
+
|
| 125 |
+
## Dataset Structure
|
| 126 |
+
|
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+
### Data Instances
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| 128 |
+
|
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+
The dataset contains two configurations, corresponding to the two tasks: `pretask`, `main` and `posttask`. `main` contains the full data collected during the main task and analyzed during our experiments. `pretask` contains the data collected in the initial verification phase before the main task, in which all translators worked on texts highlighted in the `supervised` modality. `posttask` contains the data collected in the final phase in which all translators worked on texts in the `no_highlight` modality.
|
| 130 |
+
|
| 131 |
+
### Data Fields
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+
|
| 133 |
+
A single entry in the dataframe represents a segment (~sentence) in the dataset, that was machine-translated and post-edited by a professional translator. The following fields are contained in the training set:
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+
|
| 135 |
+
|Field |Description |
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| 136 |
+
|------------------------|-------------------------------------------------------------------------------------------------------------------------------------|
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| 137 |
+
| **Identification** | |
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| 138 |
+
|`unit_id` | The full entry identifier. Format: `qe4pe-{task_id}-{src_lang}-{tgt_lang}-{doc_id}-{segment_in_doc_id}-{translator_main_task_id}`. |
|
| 139 |
+
|`wmt_id` | Identifier of the sentence in the original [WMT23](./data/setup/wmt23/wmttest2023.eng.jsonl) dataset. |
|
| 140 |
+
|`wmt_category` | Category of the document: `biomedical` or `social` |
|
| 141 |
+
|`doc_id` | The index of the document in the current configuration of the QE4PE dataset containing the current segment. |
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| 142 |
+
|`segment_in_doc_id` | The index of the segment inside the current document. |
|
| 143 |
+
|`segment_id` | The index of the segment in the current configurations (i.e. concatenating all segments from all documents in order) |
|
| 144 |
+
|`translator_pretask_id` | The identifier for the translator according to the `pretask` format before modality assignments: `tXX`. |
|
| 145 |
+
|`translator_main_id` | The identifier for the translator according to the `main` task format after modality assignments: `{highlight_modality}_tXX`. |
|
| 146 |
+
|`src_lang` | The source language of the segment. For QE4PE, this is always English (`eng`) |
|
| 147 |
+
|`tgt_lang` | The target language of the segment: either Italian (`ita`) or Dutch (`nld`). |
|
| 148 |
+
|`highlight_modality` | The highlighting modality used for the segment. Values: `no_highlight`, `oracle`, `supervised`, `unsupervised`. |
|
| 149 |
+
| **Text statistics** | |
|
| 150 |
+
|`src_num_chars` | Length of the source segment in number of characters. |
|
| 151 |
+
|`mt_num_chars` | Length of the machine-translated segment in number of characters. |
|
| 152 |
+
|`pe_num_chars` | Length of the post-edited segment in number of characters. |
|
| 153 |
+
|`src_num_words` | Length of the source segment in number of words. |
|
| 154 |
+
|`mt_num_words` | Length of the machine-translated segment in number of words. |
|
| 155 |
+
|`pe_num_words` | Length of the post-edited segment in number of words. |
|
| 156 |
+
|`num_minor_highlighted_chars` | Number of characters highlighted as minor errors in the machine-translated text. |
|
| 157 |
+
|`num_major_highlighted_chars` | Number of characters highlighted as major errors in the machine-translated text. |
|
| 158 |
+
|`num_minor_highlighted_words` | Number of words highlighted as minor errors in the machine-translated text. |
|
| 159 |
+
|`num_major_highlighted_words` | Number of words highlighted as major errors in the machine-translated text. |
|
| 160 |
+
| **Edits statistics** | |
|
| 161 |
+
|`num_words_insert` | Number of post-editing insertions computed using [jiwer](https://github.com/jitsi/jiwer). |
|
| 162 |
+
|`num_words_delete` | Number of post-editing deletions computed using [jiwer](https://github.com/jitsi/jiwer). |
|
| 163 |
+
|`num_words_substitute` | Number of post-editing substitutions computed using [jiwer](https://github.com/jitsi/jiwer). |
|
| 164 |
+
|`num_words_unchanged` | Number of post-editing hits computed using [jiwer](https://github.com/jitsi/jiwer). |
|
| 165 |
+
|`tot_words_edits` | Total of all edit types for the sentence. |
|
| 166 |
+
|`wer` | Word Error Rate score computed between `mt_text` and `pe_text` using [jiwer](https://github.com/jitsi/jiwer). |
|
| 167 |
+
|`num_chars_insert` | Number of post-editing insertions computed using [jiwer](https://github.com/jitsi/jiwer). |
|
| 168 |
+
|`num_chars_delete` | Number of post-editing deletions computed using [jiwer](https://github.com/jitsi/jiwer). |
|
| 169 |
+
|`num_chars_substitute` | Number of post-editing substitutions computed using [jiwer](https://github.com/jitsi/jiwer). |
|
| 170 |
+
|`num_chars_unchanged` | Number of post-editing hits computed using [jiwer](https://github.com/jitsi/jiwer). |
|
| 171 |
+
|`tot_chars_edits` | Total of all edit types for the sentence. |
|
| 172 |
+
|`cer` | Character Error Rate score computed between `mt_text` and `pe_text` using [jiwer](https://github.com/jitsi/jiwer). |
|
| 173 |
+
| **Translation quality**| |
|
| 174 |
+
|`mt_bleu_max` | Max BLEU score between `mt_text` and all `pe_text` for the corresponding segment using SacreBLEU with default parameters. |
|
| 175 |
+
|`mt_bleu_min` | Min BLEU score between `mt_text` and all `pe_text` for the corresponding segment using SacreBLEU with default parameters. |
|
| 176 |
+
|`mt_bleu_mean` | Mean BLEU score between `mt_text` and all `pe_text` for the corresponding segment using SacreBLEU with default parameters. |
|
| 177 |
+
|`mt_bleu_std` | Standard deviation of BLEU scores between `mt_text` and all `pe_text` for the corresponding segment using SacreBLEU with default parameters. |
|
| 178 |
+
|`mt_chrf_max` | Max chrF score between `mt_text` and all `pe_text` for the corresponding segment using SacreBLEU with default parameters. |
|
| 179 |
+
|`mt_chrf_min` | Min chrF score between `mt_text` and all `pe_text` for the corresponding segment using SacreBLEU with default parameters. |
|
| 180 |
+
|`mt_chrf_mean` | Mean chrF score between `mt_text` and all `pe_text` for the corresponding segment using SacreBLEU with default parameters. |
|
| 181 |
+
|`mt_chrf_std` | Standard deviation of chrF scores between `mt_text` and all `pe_text` for the corresponding segment using SacreBLEU with default parameters. |
|
| 182 |
+
|`mt_ter_max` | Max TER score between `mt_text` and all `pe_text` for the corresponding segment using SacreBLEU with default parameters. |
|
| 183 |
+
|`mt_ter_min` | Min TER score between `mt_text` and all `pe_text` for the corresponding segment using SacreBLEU with default parameters. |
|
| 184 |
+
|`mt_ter_mean` | Mean TER score between `mt_text` and all `pe_text` for the corresponding segment using SacreBLEU with default parameters. |
|
| 185 |
+
|`mt_ter_std` | Standard deviation of TER scores between `mt_text` and all `pe_text` for the corresponding segment using SacreBLEU with default parameters. |
|
| 186 |
+
|`mt_comet_max` | Max COMET sentence-level score for the `mt_text` and all `pe_text` for the corresponding segment using `Unbabel/wmt22-comet-da` with default parameters. |
|
| 187 |
+
|`mt_comet_min` | Min COMET sentence-level score for the `mt_text` and all `pe_text` for the corresponding segment using `Unbabel/wmt22-comet-da` with default parameters. |
|
| 188 |
+
|`mt_comet_mean` | Mean COMET sentence-level score for the `mt_text` and all `pe_text` for the corresponding segment using `Unbabel/wmt22-comet-da` with default parameters.|
|
| 189 |
+
|`mt_comet_std` | Standard deviation of COMET sentence-level scores for the `mt_text` and all `pe_text` for the corresponding segment using `Unbabel/wmt22-comet-da` with default parameters. |
|
| 190 |
+
|`mt_xcomet_qe` | `Unbabel/XCOMET-XXL` sentence-level quality estimation score for the mt_text. |
|
| 191 |
+
|`mt_xcomet_errors` | List of error spans detected by `Unbabel/XCOMET-XXL` for the mt_text. |
|
| 192 |
+
|`pe_bleu_max` | Max BLEU score between `pe_text` and all other `pe_text` for the corresponding segment using SacreBLEU with default parameters. |
|
| 193 |
+
|`pe_bleu_min` | Min BLEU score between `pe_text` and all other `pe_text` for the corresponding segment using SacreBLEU with default parameters. |
|
| 194 |
+
|`pe_bleu_mean` | Mean BLEU score between `pe_text` and all other `pe_text` for the corresponding segment using SacreBLEU with default parameters. |
|
| 195 |
+
|`pe_bleu_std` | Standard deviation of BLEU scores between `pe_text` and all other `pe_text` for the corresponding segment using SacreBLEU with default parameters. |
|
| 196 |
+
|`pe_chrf_max` | Max chrF score between `pe_text` and all other `pe_text` for the corresponding segment using SacreBLEU with default parameters. |
|
| 197 |
+
|`pe_chrf_min` | Min chrF score between `pe_text` and all other `pe_text` for the corresponding segment using SacreBLEU with default parameters. |
|
| 198 |
+
|`pe_chrf_mean` | Mean chrF score between `pe_text` and all other `pe_text` for the corresponding segment using SacreBLEU with default parameters. |
|
| 199 |
+
|`pe_chrf_std` | Standard deviation of chrF scores between `pe_text` and all other `pe_text` for the corresponding segment using SacreBLEU with default parameters. |
|
| 200 |
+
|`pe_ter_max` | Max TER score between `pe_text` and all other `pe_text` for the corresponding segment using SacreBLEU with default parameters. |
|
| 201 |
+
|`pe_ter_min` | Min TER score between `pe_text` and all other `pe_text` for the corresponding segment using SacreBLEU with default parameters. |
|
| 202 |
+
|`pe_ter_mean` | Mean TER score between `pe_text` and all other `pe_text` for the corresponding segment using SacreBLEU with default parameters. |
|
| 203 |
+
|`pe_ter_std` | Standard deviation of TER scores between `pe_text` and all other `pe_text` for the corresponding segment using SacreBLEU with default parameters. |
|
| 204 |
+
|`pe_comet_max` | Max COMET sentence-level score for the `pe_text` and all other `pe_text` for the corresponding segment using `Unbabel/wmt22-comet-da` with default parameters. |
|
| 205 |
+
|`pe_comet_min` | Min COMET sentence-level score for the `pe_text` and all other `pe_text` for the corresponding segment using `Unbabel/wmt22-comet-da` with default parameters. |
|
| 206 |
+
|`pe_comet_mean` | Mean COMET sentence-level score for the `pe_text` and all other `pe_text` for the corresponding segment using `Unbabel/wmt22-comet-da` with default parameters.|
|
| 207 |
+
|`pe_comet_std` | Standard deviation of COMET sentence-level scores for the `pe_text` and all other `pe_text` for the corresponding segment using Unbabel/wmt22-comet-da with default parameters. |
|
| 208 |
+
|`pe_xcomet_qe` | `Unbabel/XCOMET-XXL` sentence-level quality estimation score for the pe_text. |
|
| 209 |
+
|`pe_xcomet_errors` | List of error spans detected by `Unbabel/XCOMET-XXL` for the pe_text. |
|
| 210 |
+
| **Behavioral data** | |
|
| 211 |
+
|`doc_num_edits` | Total number of edits performed by the translator on the current document. Only the last edit outputs are considered valid. |
|
| 212 |
+
|`doc_edit_order` | Index corresponding to the current document edit order. If equal to `doc_id`, the document was edited in the given order. |
|
| 213 |
+
|`doc_edit_time` | Total editing time for the current document in seconds (from `start` to `end`, no times ignored) |
|
| 214 |
+
|`doc_edit_time_filtered`| Total editing time for the current document in seconds (from `start` to `end`, >5m pauses between logged actions ignored) |
|
| 215 |
+
|`doc_keys_per_min` | Keystrokes per minute computed for the current document using `doc_edit_time_filtered`. |
|
| 216 |
+
|`doc_chars_per_min` | Characters per minute computed for the current document using `doc_edit_time_filtered`. |
|
| 217 |
+
|`doc_words_per_min` | Words per minute computed for the current document using `doc_edit_time_filtered`. |
|
| 218 |
+
|`segment_num_edits` | Total number of edits performed by the translator on the current segment. Only edits for the last edit of the doc are considered valid. |
|
| 219 |
+
|`segment_edit_order` | Index corresponding to the current segment edit order (only first `enter` action counts). If equal to `segment_in_doc_id`, the segment was edited in the given order. |
|
| 220 |
+
|`segment_edit_time` | Total editing time for the current segment in seconds (summed time between `enter`-`exit` blocks) |
|
| 221 |
+
|`segment_edit_time_filtered` | Total editing time for the current segment in seconds (>5m pauses between logged actions ignored). |
|
| 222 |
+
|`segment_keys_per_min` | Keystrokes per minute computed for the current segment using `segment_edit_time_filtered`. |
|
| 223 |
+
|`segment_chars_per_min` | Characters per minute computed for the current segment using `segment_edit_time_filtered`. |
|
| 224 |
+
|`segment_words_per_min` | Words per minute computed for the current segment using `segment_edit_time_filtered`. |
|
| 225 |
+
|`num_enter_actions` | Number of `enter` actions (focus on textbox) performed by the translator on the current segment during post-editing. |
|
| 226 |
+
|`remove_highlights` | If True, the Clear Highlights button was pressed for this segment (always false for `no_highlight` modality). |
|
| 227 |
+
|**Texts and annotations**| |
|
| 228 |
+
|`src_text` | The original source segment from WMT23 requiring translation. |
|
| 229 |
+
|`mt_text` | Output of the `NLLB-3.3B` model when translating `src_text` into `tgt_lang` (default config, 5 beams) |
|
| 230 |
+
|`mt_text_highlighted` | Highlighted version of `mt_text` with potential errors according to the `highlight_modality`. |
|
| 231 |
+
|`pe_text` | Post-edited version of `mt_text` produced by a professional translator with `highlight_modality`. |
|
| 232 |
+
|`mt_pe_word_aligned` | Aligned visual representation of word-level edit operations (I = Insertion, D = Deletion, S = Substitution) (replace `\\n` with `\n` to show the three aligned rows). |
|
| 233 |
+
|`mt_pe_char_aligned` | Aligned visual representation of character-level edit operations (I = Insertion, D = Deletion, S = Substitution) (replace `\\n` with `\n` to show the three aligned rows). |
|
| 234 |
+
|`highlights` | List of dictionaries for highlighted spans with error severity and position, matching XCOMET format for word-level error annotations. |
|
| 235 |
+
|**MQM annotations (`main` config only)**| |
|
| 236 |
+
|`qa_mt_annotator_id` | Annotator ID for the MQM evaluation of `qa_mt_annotated_text`. |
|
| 237 |
+
|`qa_pe_annotator_id` | Annotator ID for the MQM evaluation of `qa_pe_annotated_text`. |
|
| 238 |
+
|`qa_mt_esa_rating` | 0-100 quality rating for the `qa_mt_annotated_text` translation, following the [ESA framework](https://aclanthology.org/2024.wmt-1.131/). |
|
| 239 |
+
|`qa_pe_esa_rating` | 0-100 quality rating for the `qa_pe_annotated_text` translation, following the [ESA framework](https://aclanthology.org/2024.wmt-1.131/). |
|
| 240 |
+
|`qa_mt_annotated_text` | Version of `mt_text` annotated with MQM errors. Might differ (only slightly) from `mt_text`, included since `qa_mt_mqm_errors` indices are computed on this string. |
|
| 241 |
+
|`qa_pe_annotated_text` | Version of `pe_text` annotated with MQM errors. Might differ (only slightly) from `pe_text`, included since `qa_pe_mqm_errors` indices are computed on this string. |
|
| 242 |
+
|`qa_mt_fixed_text` | Proposed correction of `mqm_mt_annotated_text` following MQM annotation. |
|
| 243 |
+
|`qa_pe_fixed_text` | Proposed correction of `mqm_pe_annotated_text` following MQM annotation. |
|
| 244 |
+
|`qa_mt_mqm_errors` | List of error spans detected by the MQM annotator for the `qa_mt_annotated_text`. Each error span dictionary contains the following fields: `text`: the span in `mqm_mt_annotated_text` containing an error. `text_start`: the start index of the error span in `qa_mt_annotated_text`. -1 if no annotated span is present (e.g. for omissions) `text_end`: the end index of the error span in `qa_mt_annotated_text`. -1 if no annotated span is present (e.g. for omissions) `correction`: the proposed correction in `qa_mt_fixed_text` for the error span in `qa_mt_annotated_text`. `correction_start`: the start index of the error span in `mqm_mt_fixed_text`. -1 if no corrected span is present (e.g. for additions) `correction_end`: the end index of the error span in `qa_mt_fixed_text`. -1 if no corrected span is present (e.g. for additions) `description`: an optional error description provided by the annotator. `mqm_category`: the error category assigned by the annotator for the current span. One of: Addition, Omission, Mistranslation, Inconsistency, Untranslated, Punctuation, Spelling, Grammar, Inconsistent Style, Readability, Wrong Register. `severity`: the error severity for the current span. One of: Minor, Major, Neutral. `comment`: an optional comment provided by the annotator for the current span. `edit_order`: index of the edit in the current segment edit order (starting from 1). |
|
| 245 |
+
|`qa_pe_mqm_errors` | List of error spans detected by the MQM annotator for the `qa_pe_annotated_text`. Each error span dictionary contains the following fields: `text`: the span in `qa_pe_annotated_text` containing an error. `text_start`: the start index of the error span in `qa_pe_annotated_text`. -1 if no annotated span is present (e.g. for omissions) `text_end`: the end index of the error span in `qa_pe_annotated_text`. -1 if no annotated span is present (e.g. for omissions) `correction`: the proposed correction in `qa_pe_fixed_text` for the error span in `qa_pe_annotated_text`. `correction_start`: the start index of the error span in `qa_pe_fixed_text`. -1 if no corrected span is present (e.g. for additions) `correction_end`: the end index of the error span in `qa_pe_fixed_text`. -1 if no corrected span is present (e.g. for additions) `description`: an optional error description provided by the annotator. `mqm_category`: the error category assigned by the annotator for the current span. One of: Addition, Omission, Mistranslation, Inconsistency, Untranslated, Punctuation, Spelling, Grammar, Inconsistent Style, Readability, Wrong Register. `severity`: the error severity for the current span. One of: Minor, Major, Neutral. `comment`: an optional comment provided by the annotator for the current span. `edit_order`: index of the edit in the current segment edit order (starting from 1). |
|
| 246 |
+
|
| 247 |
+
### Data Splits
|
| 248 |
+
|
| 249 |
+
|`config` | `split`| |
|
| 250 |
+
|------------------------------------:|-------:|--------------------------------------------------------------:|
|
| 251 |
+
|`main` | `train`| 8100 (51 docs i.e. 324 sents x 25 translators) |
|
| 252 |
+
|`pretask` | `train`| 950 (6 docs i.e. 38 sents x 25 translators) |
|
| 253 |
+
|`posttask` | `train`| 1200 (8 docs i.e. 50 sents x 24 translators) |
|
| 254 |
+
|`pretask_questionnaire` | `train`| 26 (all translators, including replaced/replacements) |
|
| 255 |
+
|`posttask_highlight_questionnaire` | `train`| 19 (all translators for highlight modalities + 1 replacement) |
|
| 256 |
+
|`posttask_no_highlight_questionnaire`| `train`| 6 (all translators for `no_highlight` modality) |
|
| 257 |
+
|
| 258 |
+
#### Train Split
|
| 259 |
+
|
| 260 |
+
The `train` split contains the totality of triplets (or pairs, when translation from scratch is performed) annotated with behavioral data produced during the translation.
|
| 261 |
+
|
| 262 |
+
The following is an example of the subject `oracle_t1` post-editing for segment `3` of `doc20` in the `eng-nld` direction of the `main` task. The fields `mt_pe_word_aligned` and `mt_pe_char_aligned` are shown over three lines to provide a visual understanding of their contents.
|
| 263 |
+
|
| 264 |
+
```python
|
| 265 |
+
{
|
| 266 |
+
# Identification
|
| 267 |
+
"unit_id": "qe4pe-main-eng-nld-20-3-oracle_t1",
|
| 268 |
+
"wmt_id": "doc5",
|
| 269 |
+
"wmt_category": "biomedical",
|
| 270 |
+
"doc_id": 20,
|
| 271 |
+
"segment_in_doc_id": 3,
|
| 272 |
+
"segment_id": 129,
|
| 273 |
+
"translator_pretask_id": "t4",
|
| 274 |
+
"translator_main_id": "oracle_t1",
|
| 275 |
+
"src_lang": "eng",
|
| 276 |
+
"tgt_lang": "nld",
|
| 277 |
+
"highlight_modality": "oracle",
|
| 278 |
+
# Text statistics
|
| 279 |
+
"src_num_chars": 104,
|
| 280 |
+
"mt_num_chars": 136,
|
| 281 |
+
"pe_num_chars": 106,
|
| 282 |
+
"src_num_words": 15,
|
| 283 |
+
"mt_num_words": 16,
|
| 284 |
+
"pe_num_words": 16,
|
| 285 |
+
# Edits statistics
|
| 286 |
+
"num_words_insert": 0,
|
| 287 |
+
"num_words_delete": 0,
|
| 288 |
+
"num_words_substitute": 1,
|
| 289 |
+
"num_words_unchanged": 15,
|
| 290 |
+
"tot_words_edits": 1,
|
| 291 |
+
"wer": 0.0625,
|
| 292 |
+
"num_chars_insert": 0,
|
| 293 |
+
"num_chars_delete": 0,
|
| 294 |
+
"num_chars_substitute": 6,
|
| 295 |
+
"num_chars_unchanged": 100,
|
| 296 |
+
"tot_chars_edits": 6,
|
| 297 |
+
"cer": 0.0566,
|
| 298 |
+
# Translation quality
|
| 299 |
+
"mt_bleu_max": 100.0,
|
| 300 |
+
"mt_bleu_min": 7.159,
|
| 301 |
+
"mt_bleu_mean": 68.687,
|
| 302 |
+
"mt_bleu_std": 31.287,
|
| 303 |
+
"mt_chrf_max": 100.0,
|
| 304 |
+
"mt_chrf_min": 45.374,
|
| 305 |
+
"mt_chrf_mean": 83.683,
|
| 306 |
+
"mt_chrf_std": 16.754,
|
| 307 |
+
"mt_ter_max": 100.0,
|
| 308 |
+
"mt_ter_min": 0.0,
|
| 309 |
+
"mt_ter_mean": 23.912,
|
| 310 |
+
"mt_ter_std": 29.274,
|
| 311 |
+
"mt_comet_max": 0.977,
|
| 312 |
+
"mt_comet_min": 0.837,
|
| 313 |
+
"mt_comet_mean": 0.94,
|
| 314 |
+
"mt_comet_std": 0.042,
|
| 315 |
+
"mt_xcomet_qe": 0.985,
|
| 316 |
+
"mt_xcomet_errors": "[]",
|
| 317 |
+
"pe_bleu_max": 100.0,
|
| 318 |
+
"pe_bleu_min": 11.644,
|
| 319 |
+
"pe_bleu_mean": 61.335,
|
| 320 |
+
"pe_bleu_std": 28.617,
|
| 321 |
+
"pe_chrf_max": 100.0,
|
| 322 |
+
"pe_chrf_min": 53.0,
|
| 323 |
+
"pe_chrf_mean": 79.173,
|
| 324 |
+
"pe_chrf_std": 13.679,
|
| 325 |
+
"pe_ter_max": 100.0,
|
| 326 |
+
"pe_ter_min": 0.0,
|
| 327 |
+
"pe_ter_mean": 28.814,
|
| 328 |
+
"pe_ter_std": 28.827,
|
| 329 |
+
"pe_comet_max": 0.977,
|
| 330 |
+
"pe_comet_min": 0.851,
|
| 331 |
+
"pe_comet_mean": 0.937,
|
| 332 |
+
"pe_comet_std": 0.035,
|
| 333 |
+
"pe_xcomet_qe": 0.984,
|
| 334 |
+
"pe_xcomet_errors": "[]",
|
| 335 |
+
# Behavioral data
|
| 336 |
+
"doc_num_edits": 103,
|
| 337 |
+
"doc_edit_order": 20,
|
| 338 |
+
"doc_edit_time": 118,
|
| 339 |
+
"doc_edit_time_filtered": 118,
|
| 340 |
+
"doc_keys_per_min": 52.37,
|
| 341 |
+
"doc_chars_per_min": 584.24,
|
| 342 |
+
"doc_words_per_min": 79.83,
|
| 343 |
+
"segment_num_edits": 9,
|
| 344 |
+
"segment_edit_order": 3,
|
| 345 |
+
"segment_edit_time": 9,
|
| 346 |
+
"segment_edit_time_filtered": 9,
|
| 347 |
+
"segment_keys_per_min": 60.0,
|
| 348 |
+
"segment_chars_per_min": 906.67,
|
| 349 |
+
"segment_words_per_min": 106.67,
|
| 350 |
+
"num_enter_actions": 2,
|
| 351 |
+
"remove_highlights": False,
|
| 352 |
+
# Texts and annotations
|
| 353 |
+
"src_text": "The speed of its emerging growth frequently outpaces the development of quality assurance and education.",
|
| 354 |
+
"mt_text": "De snelheid van de opkomende groei is vaak sneller dan de ontwikkeling van kwaliteitsborging en onderwijs.",
|
| 355 |
+
"mt_text_highlighted": "De snelheid van de opkomende groei is vaak <minor>sneller</minor> dan de ontwikkeling van kwaliteitsborging en <major>onderwijs.</major>",
|
| 356 |
+
"pe_text": "De snelheid van de opkomende groei is vaak sneller dan de ontwikkeling van kwaliteitsborging en opleiding.",
|
| 357 |
+
"mt_pe_word_aligned": "MT: De snelheid van de opkomende groei is vaak sneller dan de ontwikkeling van kwaliteitsborging en onderwijs.\n" \
|
| 358 |
+
"PE: De snelheid van de opkomende groei is vaak sneller dan de ontwikkeling van kwaliteitsborging en opleiding.\n" \
|
| 359 |
+
" S",
|
| 360 |
+
"mt_pe_char_aligned": "MT: De snelheid van de opkomende groei is vaak sneller dan de ontwikkeling van kwaliteitsborging en onderwijs.\n" \
|
| 361 |
+
"PE: De snelheid van de opkomende groei is vaak sneller dan de ontwikkeling van kwaliteitsborging en opleiding.\n" \
|
| 362 |
+
" SS SS SS ",
|
| 363 |
+
"highlights": """[
|
| 364 |
+
{
|
| 365 |
+
'text': 'sneller',
|
| 366 |
+
'severity': 'minor',
|
| 367 |
+
'start': 43,
|
| 368 |
+
'end': 50
|
| 369 |
+
},
|
| 370 |
+
{
|
| 371 |
+
'text': 'onderwijs.',
|
| 372 |
+
'severity': 'major',
|
| 373 |
+
'start': 96,
|
| 374 |
+
'end': 106
|
| 375 |
+
}
|
| 376 |
+
]"""
|
| 377 |
+
# QA annotations
|
| 378 |
+
"qa_mt_annotator_id": 'qa_nld_3',
|
| 379 |
+
"qa_pe_annotator_id": 'qa_nld_1',
|
| 380 |
+
"qa_mt_esa_rating": 100.0,
|
| 381 |
+
"qa_pe_esa_rating": 80.0,
|
| 382 |
+
"qa_mt_annotated_text": "De snelheid van de opkomende groei is vaak sneller dan de ontwikkeling van kwaliteitsborging en onderwijs.",
|
| 383 |
+
"qa_pe_annotated_text": "De snelheid van de opkomende groei is vaak sneller dan de ontwikkeling van kwaliteitsborging en opleiding.",
|
| 384 |
+
"qa_mt_fixed_text": "De snelheid van de opkomende groei is vaak sneller dan de ontwikkeling van kwaliteitsborging en onderwijs.",
|
| 385 |
+
"qa_pe_fixed_text": "De snelheid van de ontluikende groei overtreft vaak de ontwikkeling van kwaliteitsborging en onderwijs.",
|
| 386 |
+
"qa_mt_mqm_errors": "[]",
|
| 387 |
+
"qa_pe_mqm_errors": """[
|
| 388 |
+
{
|
| 389 |
+
"text": "opkomende",
|
| 390 |
+
"text_start": 19,
|
| 391 |
+
"text_end": 28,
|
| 392 |
+
"correction":
|
| 393 |
+
"ontluikende",
|
| 394 |
+
"correction_start": 19,
|
| 395 |
+
"correction_end": 30,
|
| 396 |
+
"description": "Mistranslation - not the correct word",
|
| 397 |
+
"mqm_category": "Mistranslation",
|
| 398 |
+
"severity": "Minor",
|
| 399 |
+
"comment": "",
|
| 400 |
+
"edit_order": 1
|
| 401 |
+
}
|
| 402 |
+
]"""
|
| 403 |
+
|
| 404 |
+
}
|
| 405 |
+
```
|
| 406 |
+
|
| 407 |
+
The text is provided as-is, without further preprocessing or tokenization.
|
| 408 |
+
|
| 409 |
+
### Dataset Creation
|
| 410 |
+
|
| 411 |
+
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).
|
| 412 |
+
|
| 413 |
+
### QA Annotations
|
| 414 |
+
|
| 415 |
+
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.
|
| 416 |
+
|
| 417 |
+
## Additional Information
|
| 418 |
+
|
| 419 |
+
### Metric signatures
|
| 420 |
+
|
| 421 |
+
The following signatures correspond to the metrics reported in the processed dataframes:
|
| 422 |
+
|
| 423 |
+
```shell
|
| 424 |
+
# Computed using SacreBLEU: https://github.com/mjpost/sacrebleu
|
| 425 |
+
BLEU: case:mixed|eff:yes|tok:13a|smooth:exp|version:2.3.1
|
| 426 |
+
ChrF: case:mixed|eff:yes|nc:6|nw:0|space:no|version:2.3.1
|
| 427 |
+
TER: case:lc|tok:tercom|norm:no|punct:yes|asian:no|version:2.3.1
|
| 428 |
+
|
| 429 |
+
# Computed using Unbabel COMET: https://github.com/Unbabel/COMET
|
| 430 |
+
Comet: Python3.11.9|Comet2.2.2|fp32|Unbabel/wmt22-comet-da
|
| 431 |
+
XComet: Python3.10.12|Comet2.2.1|fp32|Unbabel/XCOMET-XXL
|
| 432 |
+
```
|
| 433 |
+
|
| 434 |
+
### Dataset Curators
|
| 435 |
+
|
| 436 |
+
For problems related to this 🤗 Datasets version, please contact me at [[email protected]](mailto:[email protected]).
|
| 437 |
+
|
| 438 |
+
### Citation Information
|
| 439 |
+
|
| 440 |
+
```bibtex
|
| 441 |
+
@misc{sarti-etal-2024-qe4pe,
|
| 442 |
+
title={{QE4PE}: Word-level Quality Estimation for Human Post-Editing},
|
| 443 |
+
author={Gabriele Sarti and Vilém Zouhar and Grzegorz Chrupała and Ana Guerberof-Arenas and Malvina Nissim and Arianna Bisazza},
|
| 444 |
+
year={2025},
|
| 445 |
+
eprint={2503.03044},
|
| 446 |
+
archivePrefix={arXiv},
|
| 447 |
+
primaryClass={cs.CL},
|
| 448 |
+
url={https://arxiv.org/abs/2503.03044},
|
| 449 |
+
}
|
| 450 |
```
|