Datasets:
Tasks:
Translation
Formats:
csv
Size:
10K - 100K
Tags:
machine-translation
quality-estimation
post-editing
translation
behavioral-data
multidimensional-quality-metric
License:
Corrected MQM annotations
Browse files- README.md +68 -17
- task/main/doc_id_map.json +1 -0
- task/main/processed_main.csv +2 -2
README.md
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- post-editing
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- translation
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- behavioral-data
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language_creators:
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- machine-generated
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- expert-generated
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### Dataset Summary
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This dataset provides a convenient access to the processed `main` and `
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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.
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### News 📢
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**October 2024**: The QE4PE dataset is released on the HuggingFace Hub! 🎉
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### Repository Structure
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│ └── ... # Configurations reporting the exact questionnaires questions and options.
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├── setup/
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│ ├── highlights/ # Outputs of word-level QE strategies used to setup highlighted spans in the tasks
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│ ├── processed/ # Intermediate outputs of the selection process for the main task
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│ └── wmt23/ # Original collection of WMT23 sources and machine-translated outputs
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└── task/
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### Data Instances
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The dataset contains two configurations, corresponding to the two tasks: `main` and `
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### Data Fields
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|`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). |
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|`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). |
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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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### Data Splits
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|`config` | `split`| |
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|------------------------------------:|-------:|--------------------------------------------------------------:|
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|`main` | `train`| 8100 (51 docs i.e. 324 sents x 25 translators) |
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|`pretask` | `train`| 950 (6 docs i.e. 38 sents x 25 translators)
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|`posttask` | `train`| 1200 (8 docs i.e. 50 sents x 24 translators)
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|`pretask_questionnaire` | `train`| 26 (all translators, including replaced/replacements) |
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|`posttask_highlight_questionnaire` | `train`| 19 (all translators for highlight modalities + 1 replacement) |
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|`posttask_no_highlight_questionnaire`| `train`| 6 (all translators for `no_highlight` modality) |
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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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```
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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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## Additional Information
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### Metric signatures
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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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### Dataset Summary
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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.
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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.
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### News 📢
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**January 2025**: MQM annotations are now available for the `main` task.
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**October 2024**: The QE4PE dataset is released on the HuggingFace Hub! 🎉
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### Repository Structure
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│ └── ... # Configurations reporting the exact questionnaires questions and options.
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├── setup/
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│ ├── highlights/ # Outputs of word-level QE strategies used to setup highlighted spans in the tasks
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│ ├── mqm/ # MQM annotations for the main task
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│ ├── processed/ # Intermediate outputs of the selection process for the main task
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│ └── wmt23/ # Original collection of WMT23 sources and machine-translated outputs
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└── task/
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### Data Instances
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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.
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### Data Fields
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|`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). |
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|`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). |
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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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|`qa_pe_annotator_id` | Annotator ID for the MQM evaluation of `qa_pe_annotated_text`. |
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|`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/). |
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|`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/). |
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|`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. |
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|`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. |
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|`qa_mt_fixed_text` | Proposed correction of `mqm_mt_annotated_text` following MQM annotation. |
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|`qa_pe_fixed_text` | Proposed correction of `mqm_pe_annotated_text` following MQM annotation. |
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|`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). |
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|`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). |
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### Data Splits
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|`config` | `split`| |
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|------------------------------------:|-------:|--------------------------------------------------------------:|
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|`main` | `train`| 8100 (51 docs i.e. 324 sents x 25 translators) |
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|`pretask` | `train`| 950 (6 docs i.e. 38 sents x 25 translators) |
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|`posttask` | `train`| 1200 (8 docs i.e. 50 sents x 24 translators) |
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|`pretask_questionnaire` | `train`| 26 (all translators, including replaced/replacements) |
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|`posttask_highlight_questionnaire` | `train`| 19 (all translators for highlight modalities + 1 replacement) |
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|`posttask_no_highlight_questionnaire`| `train`| 6 (all translators for `no_highlight` modality) |
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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 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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### MQM 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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task/main/doc_id_map.json
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"unsupervised": "../../setup/highlights/unsupervised/grote_files"
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},
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"original_config": "../../setup/wmt23/wmttest2023.eng.jsonl",
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"map":{
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"doc1": "doc39",
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"doc2": "doc33",
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"unsupervised": "../../setup/highlights/unsupervised/grote_files"
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"original_config": "../../setup/wmt23/wmttest2023.eng.jsonl",
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"qa_path": "../../setup/qa/qa_df.csv",
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"map":{
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"doc1": "doc39",
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"doc2": "doc33",
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task/main/processed_main.csv
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version https://git-lfs.github.com/spec/v1
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oid sha256:228b65c4fd8adf1ebf5f7a7cf4dc7e8d748d01751735a956bb851ecc207853e5
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size 23222322
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