Datasets:
File size: 33,487 Bytes
e28b6c0 ada5861 77b4625 049fbc7 e28b6c0 ada5861 3f9fba0 ada5861 3f9fba0 ada5861 217cc91 ada5861 77b4625 ada5861 4281e50 ada5861 83dc149 ada5861 964cd08 4281e50 ada5861 964cd08 ada5861 964cd08 217cc91 964cd08 ada5861 964cd08 ada5861 964cd08 217cc91 ada5861 964cd08 ada5861 217cc91 ada5861 4281e50 ada5861 217cc91 5d71c53 ada5861 217cc91 77b4625 217cc91 ada5861 217cc91 ada5861 217cc91 ada5861 217cc91 e781efb 217cc91 ada5861 217cc91 ada5861 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 |
---
language:
- en
- it
- nl
license:
- apache-2.0
tags:
- machine-translation
- quality-estimation
- post-editing
- translation
- behavioral-data
language_creators:
- machine-generated
- expert-generated
annotations_creators:
- machine-generated
pretty_name: qe4pe
size_categories:
- 10K<n<100K
source_datasets:
- Unbabel/TowerEval-Data-v0.1
task_categories:
- translation
configs:
- config_name: main
data_files:
- split: train
path: task/main/processed_main.csv
- config_name: pretask
data_files:
- split: train
path: task/pretask/processed_pretask.csv
- config_name: posttask
data_files:
- split: train
path: task/posttask/processed_posttask.csv
- config_name: pretask_questionnaire
data_files:
- split: train
path: questionnaires/pretask_results.csv
- config_name: posttask_highlight_questionnaire
data_files:
- split: train
path: questionnaires/posttask_highlight_results.csv
- config_name: posttask_no_highlight_questionnaire
data_files:
- split: train
path: questionnaires/posttask_no_highlight_results.csv
---
# Quality Estimation for Post-Editing (QE4PE)
*For more details on QE4PE, see our [paper](TBD) and our [Github repository](https://github.com/gsarti/qe4pe)*
## Dataset Description
- **Source:** [Github](https://github.com/gsarti/qe4pe)
- **Paper:** [Arxiv](TBD)
- **Point of Contact:** [Gabriele Sarti](mailto:[email protected])
[Gabriele Sarti](https://gsarti.com) • [Vilém Zouhar](https://vilda.net/) • [Malvina Nissim](https://malvinanissim.github.io/) • [Grzegorz Chrupała](https://grzegorz.chrupala.me/) • [Ana Guerberof Arenas](https://scholar.google.com/citations?user=i6bqaTsAAAAJ) • [Arianna Bisazza](https://www.cs.rug.nl/~bisazza/)
<img src="TBD" alt="TBD" width="600"/>
>Abstract TBD
### Dataset Summary
This dataset provides a convenient access to the processed `main` and `pretask` splits of the QE4PE dataset. 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.
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.
### News 📢
**October 2024**: The QE4PE dataset is released on the HuggingFace Hub! 🎉
### Repository Structure
The repository is organized as follows:
```shell
qe4pe/
├── questionnaires/ # Configs and results for pre- and post-task questionnaires for translators
│ ├── pretask_results.csv # Results of the pretask questionnaire, corresponding to the `pretask_questionnaire` configuration
│ ├── posttask_highlight_results.csv # Results of the posttask questionnaire for highlighted modalities, corresponding to the `posttask_highlight_questionnaire` configuration
│ ├── posttask_no_highlight_results.csv # Results of the posttask questionnaire for the `no_highlight` modality, corresponding to the `posttask_no_highlight_questionnaire` configuration
│ └── ... # Configurations reporting the exact questionnaires questions and options.
├── setup/
│ ├── highlights/ # Outputs of word-level QE strategies used to setup highlighted spans in the tasks
│ ├── processed/ # Intermediate outputs of the selection process for the main task
│ └── wmt23/ # Original collection of WMT23 sources and machine-translated outputs
└── task/
├── example/ # Example folder with task structure
├── main/ # Main task data, logs, outputs and guidelines
│ ├── ...
│ ├── processed_main.csv # Processed main task data, corresponds to the `main` configuration
│ └── README.md # Details about the main task
├── posttask/ # Posttask task data, logs, outputs and guidelines
│ ├── ...
│ ├── processed_main.csv # Processed posttask task data, corresponds to the `posttask` configuration
│ └── README.md # Details about the post-task
└── pretask/ # Pretask data, logs, outputs and guidelines
├── ...
├── processed_pretask.csv # Processed pretask data, corresponds to the `pretask` configuration
└── README.md # Details about the pretask
```
### Languages
The language data of QE4PE is in English (BCP-47 `en`), Italian (BCP-47 `it`) and Dutch (BCP-47 `nl`).
## Dataset Structure
### Data Instances
The dataset contains two configurations, corresponding to the two tasks: `main` and `pretask`. `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 begins.
### Data Fields
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:
|Field |Description |
|------------------------|-------------------------------------------------------------------------------------------------------------------------------------|
| **Identification** | |
|`unit_id` | The full entry identifier. Format: `qe4pe-{task_id}-{src_lang}-{tgt_lang}-{doc_id}-{segment_in_doc_id}-{translator_main_task_id}`. |
|`wmt_id` | Identifier of the sentence in the original [WMT23](./data/setup/wmt23/wmttest2023.eng.jsonl) dataset. |
|`wmt_category` | Category of the document: `biomedical` or `social` |
|`doc_id` | The index of the document in the current configuration of the QE4PE dataset containing the current segment. |
|`segment_in_doc_id` | The index of the segment inside the current document. |
|`segment_id` | The index of the segment in the current configurations (i.e. concatenating all segments from all documents in order) |
|`translator_pretask_id` | The identifier for the translator according to the `pretask` format before modality assignments: `tXX`. |
|`translator_main_id` | The identifier for the translator according to the `main` task format after modality assignments: `{highlight_modality}_tXX`. |
|`src_lang` | The source language of the segment. For QE4PE, this is always English (`eng`) |
|`tgt_lang` | The target language of the segment: either Italian (`ita`) or Dutch (`nld`). |
|`highlight_modality` | The highlighting modality used for the segment. Values: `no_highlight`, `oracle`, `supervised`, `unsupervised`. |
| **Text statistics** | |
|`src_num_chars` | Length of the source segment in number of characters. |
|`mt_num_chars` | Length of the machine-translated segment in number of characters. |
|`pe_num_chars` | Length of the post-edited segment in number of characters. |
|`src_num_words` | Length of the source segment in number of words. |
|`mt_num_words` | Length of the machine-translated segment in number of words. |
|`pe_num_words` | Length of the post-edited segment in number of words. |
|`num_minor_highlighted_chars` | Number of characters highlighted as minor errors in the machine-translated text. |
|`num_major_highlighted_chars` | Number of characters highlighted as major errors in the machine-translated text. |
|`num_minor_highlighted_words` | Number of words highlighted as minor errors in the machine-translated text. |
|`num_major_highlighted_words` | Number of words highlighted as major errors in the machine-translated text. |
| **Edits statistics** | |
|`num_words_insert` | Number of post-editing insertions computed using [jiwer](https://github.com/jitsi/jiwer). |
|`num_words_delete` | Number of post-editing deletions computed using [jiwer](https://github.com/jitsi/jiwer). |
|`num_words_substitute` | Number of post-editing substitutions computed using [jiwer](https://github.com/jitsi/jiwer). |
|`num_words_unchanged` | Number of post-editing hits computed using [jiwer](https://github.com/jitsi/jiwer). |
|`tot_words_edits` | Total of all edit types for the sentence. |
|`wer` | Word Error Rate score computed between `mt_text` and `pe_text` using [jiwer](https://github.com/jitsi/jiwer). |
|`num_chars_insert` | Number of post-editing insertions computed using [jiwer](https://github.com/jitsi/jiwer). |
|`num_chars_delete` | Number of post-editing deletions computed using [jiwer](https://github.com/jitsi/jiwer). |
|`num_chars_substitute` | Number of post-editing substitutions computed using [jiwer](https://github.com/jitsi/jiwer). |
|`num_chars_unchanged` | Number of post-editing hits computed using [jiwer](https://github.com/jitsi/jiwer). |
|`tot_chars_edits` | Total of all edit types for the sentence. |
|`cer` | Character Error Rate score computed between `mt_text` and `pe_text` using [jiwer](https://github.com/jitsi/jiwer). |
| **Translation quality**| |
|`mt_bleu_max` | Max BLEU score between `mt_text` and all `pe_text` for the corresponding segment using SacreBLEU with default parameters. |
|`mt_bleu_min` | Min BLEU score between `mt_text` and all `pe_text` for the corresponding segment using SacreBLEU with default parameters. |
|`mt_bleu_mean` | Mean BLEU score between `mt_text` and all `pe_text` for the corresponding segment using SacreBLEU with default parameters. |
|`mt_bleu_std` | Standard deviation of BLEU scores between `mt_text` and all `pe_text` for the corresponding segment using SacreBLEU with default parameters. |
|`mt_chrf_max` | Max chrF score between `mt_text` and all `pe_text` for the corresponding segment using SacreBLEU with default parameters. |
|`mt_chrf_min` | Min chrF score between `mt_text` and all `pe_text` for the corresponding segment using SacreBLEU with default parameters. |
|`mt_chrf_mean` | Mean chrF score between `mt_text` and all `pe_text` for the corresponding segment using SacreBLEU with default parameters. |
|`mt_chrf_std` | Standard deviation of chrF scores between `mt_text` and all `pe_text` for the corresponding segment using SacreBLEU with default parameters. |
|`mt_ter_max` | Max TER score between `mt_text` and all `pe_text` for the corresponding segment using SacreBLEU with default parameters. |
|`mt_ter_min` | Min TER score between `mt_text` and all `pe_text` for the corresponding segment using SacreBLEU with default parameters. |
|`mt_ter_mean` | Mean TER score between `mt_text` and all `pe_text` for the corresponding segment using SacreBLEU with default parameters. |
|`mt_ter_std` | Standard deviation of TER scores between `mt_text` and all `pe_text` for the corresponding segment using SacreBLEU with default parameters. |
|`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. |
|`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. |
|`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.|
|`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. |
|`mt_xcomet_qe` | `Unbabel/XCOMET-XXL` sentence-level quality estimation score for the mt_text. |
|`mt_xcomet_errors` | List of error spans detected by `Unbabel/XCOMET-XXL` for the mt_text. |
|`pe_bleu_max` | Max BLEU score between `pe_text` and all other `pe_text` for the corresponding segment using SacreBLEU with default parameters. |
|`pe_bleu_min` | Min BLEU score between `pe_text` and all other `pe_text` for the corresponding segment using SacreBLEU with default parameters. |
|`pe_bleu_mean` | Mean BLEU score between `pe_text` and all other `pe_text` for the corresponding segment using SacreBLEU with default parameters. |
|`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. |
|`pe_chrf_max` | Max chrF score between `pe_text` and all other `pe_text` for the corresponding segment using SacreBLEU with default parameters. |
|`pe_chrf_min` | Min chrF score between `pe_text` and all other `pe_text` for the corresponding segment using SacreBLEU with default parameters. |
|`pe_chrf_mean` | Mean chrF score between `pe_text` and all other `pe_text` for the corresponding segment using SacreBLEU with default parameters. |
|`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. |
|`pe_ter_max` | Max TER score between `pe_text` and all other `pe_text` for the corresponding segment using SacreBLEU with default parameters. |
|`pe_ter_min` | Min TER score between `pe_text` and all other `pe_text` for the corresponding segment using SacreBLEU with default parameters. |
|`pe_ter_mean` | Mean TER score between `pe_text` and all other `pe_text` for the corresponding segment using SacreBLEU with default parameters. |
|`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. |
|`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. |
|`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. |
|`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.|
|`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. |
|`pe_xcomet_qe` | `Unbabel/XCOMET-XXL` sentence-level quality estimation score for the pe_text. |
|`pe_xcomet_errors` | List of error spans detected by `Unbabel/XCOMET-XXL` for the pe_text. |
| **Behavioral data** | |
|`doc_num_edits` | Total number of edits performed by the translator on the current document. Only the last edit outputs are considered valid. |
|`doc_edit_order` | Index corresponding to the current document edit order. If equal to `doc_id`, the document was edited in the given order. |
|`doc_edit_time` | Total editing time for the current document in seconds (from `start` to `end`, no times ignored) |
|`doc_edit_time_filtered`| Total editing time for the current document in seconds (from `start` to `end`, >5m pauses between logged actions ignored) |
|`doc_keys_per_min` | Keystrokes per minute computed for the current document using `doc_edit_time_filtered`. |
|`doc_chars_per_min` | Characters per minute computed for the current document using `doc_edit_time_filtered`. |
|`doc_words_per_min` | Words per minute computed for the current document using `doc_edit_time_filtered`. |
|`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. |
|`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. |
|`segment_edit_time` | Total editing time for the current segment in seconds (summed time between `enter`-`exit` blocks) |
|`segment_edit_time_filtered` | Total editing time for the current segment in seconds (>5m pauses between logged actions ignored). |
|`segment_keys_per_min` | Keystrokes per minute computed for the current segment using `segment_edit_time_filtered`. |
|`segment_chars_per_min` | Characters per minute computed for the current segment using `segment_edit_time_filtered`. |
|`segment_words_per_min` | Words per minute computed for the current segment using `segment_edit_time_filtered`. |
|`num_enter_actions` | Number of `enter` actions (focus on textbox) performed by the translator on the current segment during post-editing. |
|`remove_highlights` | If True, the Clear Highlights button was pressed for this segment (always false for `no_highlight` modality). |
|**Texts and annotations**| |
|`src_text` | The original source segment from WMT23 requiring translation. |
|`mt_text` | Output of the `NLLB-3.3B` model when translating `src_text` into `tgt_lang` (default config, 5 beams) |
|`mt_text_highlighted` | Highlighted version of `mt_text` with potential errors according to the `highlight_modality`. |
|`pe_text` | Post-edited version of `mt_text` produced by a professional translator with `highlight_modality`. |
|`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). |
|`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). |
|`highlights` | List of dictionaries for highlighted spans with error severity and position, matching XCOMET format for word-level error annotations. |
|**MQM annotations (`main` config only)**| |
|`mqm_mt_annotator_id` | Annotator ID for the MQM evaluation of `mqm_mt_annotated_text`. |
|`mqm_pe_annotator_id` | Annotator ID for the MQM evaluation of `mqm_pe_annotated_text`. |
|`mqm_mt_rating` | 0-100 quality rating for the `mqm_mt_annotated_text` translation. |
|`mqm_pe_rating` | 0-100 quality rating for the `mqm_pe_annotated_text` translation. |
|`mqm_mt_annotated_text` | Version of `mt_text` annotated with MQM errors. Might differ (only slightly) from `mt_text`, included since `mqm_mt_errors` indices are computed on this string. |
|`mqm_pe_annotated_text` | Version of `pe_text` annotated with MQM errors. Might differ (only slightly) from `pe_text`, included since `mqm_pe_errors` indices are computed on this string. |
|`mqm_mt_fixed_text` | Proposed correction of `mqm_mt_annotated_text` following MQM annotation. |
|`mqm_pe_fixed_text` | Proposed correction of `mqm_pe_annotated_text` following MQM annotation. |
|`mqm_mt_errors` | List of error spans detected by the MQM annotator for the `mqm_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 `mqm_mt_annotated_text`. -1 if no annotated span is present (e.g. for omissions) `text_end`: the end index of the error span in `mqm_mt_annotated_text`. -1 if no annotated span is present (e.g. for omissions) `correction`: the proposed correction in `mqm_mt_fixed_text` for the error span in `mqm_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 `mqm_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). |
|`mqm_pe_errors` | List of error spans detected by the MQM annotator for the `mqm_pe_annotated_text`. Each error span dictionary contains the following fields: `text`: the span in `mqm_pe_annotated_text` containing an error. `text_start`: the start index of the error span in `mqm_pe_annotated_text`. -1 if no annotated span is present (e.g. for omissions) `text_end`: the end index of the error span in `mqm_pe_annotated_text`. -1 if no annotated span is present (e.g. for omissions) `correction`: the proposed correction in `mqm_pe_fixed_text` for the error span in `mqm_pe_annotated_text`. `correction_start`: the start index of the error span in `mqm_pe_fixed_text`. -1 if no corrected span is present (e.g. for additions) `correction_end`: the end index of the error span in `mqm_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). |
### Data Splits
|`config` | `split`| |
|------------------------------------:|-------:|--------------------------------------------------------------:|
|`main` | `train`| 8100 (51 docs i.e. 324 sents x 25 translators) |
|`pretask` | `train`| 950 (6 docs i.e. 38 sents x 25 translators) |
|`posttask` | `train`| 1200 (8 docs i.e. 50 sents x 24 translators) |
|`pretask_questionnaire` | `train`| 26 (all translators, including replaced/replacements) |
|`posttask_highlight_questionnaire` | `train`| 19 (all translators for highlight modalities + 1 replacement) |
|`posttask_no_highlight_questionnaire`| `train`| 6 (all translators for `no_highlight` modality) |
#### Train Split
The `train` split contains the totality of triplets (or pairs, when translation from scratch is performed) annotated with behavioral data produced during the translation.
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.
```python
{
# Identification
"unit_id": "qe4pe-main-eng-nld-20-3-oracle_t1",
"wmt_id": "doc5",
"wmt_category": "biomedical",
"doc_id": 20,
"segment_in_doc_id": 3,
"segment_id": 129,
"translator_pretask_id": "t4",
"translator_main_id": "oracle_t1",
"src_lang": "eng",
"tgt_lang": "nld",
"highlight_modality": "oracle",
# Text statistics
"src_num_chars": 104,
"mt_num_chars": 136,
"pe_num_chars": 106,
"src_num_words": 15,
"mt_num_words": 16,
"pe_num_words": 16,
# Edits statistics
"num_words_insert": 0,
"num_words_delete": 0,
"num_words_substitute": 1,
"num_words_unchanged": 15,
"tot_words_edits": 1,
"wer": 0.0625,
"num_chars_insert": 0,
"num_chars_delete": 0,
"num_chars_substitute": 6,
"num_chars_unchanged": 100,
"tot_chars_edits": 6,
"cer": 0.0566,
# Translation quality
"mt_bleu_max": 100.0,
"mt_bleu_min": 7.159,
"mt_bleu_mean": 68.687,
"mt_bleu_std": 31.287,
"mt_chrf_max": 100.0,
"mt_chrf_min": 45.374,
"mt_chrf_mean": 83.683,
"mt_chrf_std": 16.754,
"mt_ter_max": 100.0,
"mt_ter_min": 0.0,
"mt_ter_mean": 23.912,
"mt_ter_std": 29.274,
"mt_comet_max": 0.977,
"mt_comet_min": 0.837,
"mt_comet_mean": 0.94,
"mt_comet_std": 0.042,
"mt_xcomet_qe": 0.985,
"mt_xcomet_errors": "[]",
"pe_bleu_max": 100.0,
"pe_bleu_min": 11.644,
"pe_bleu_mean": 61.335,
"pe_bleu_std": 28.617,
"pe_chrf_max": 100.0,
"pe_chrf_min": 53.0,
"pe_chrf_mean": 79.173,
"pe_chrf_std": 13.679,
"pe_ter_max": 100.0,
"pe_ter_min": 0.0,
"pe_ter_mean": 28.814,
"pe_ter_std": 28.827,
"pe_comet_max": 0.977,
"pe_comet_min": 0.851,
"pe_comet_mean": 0.937,
"pe_comet_std": 0.035,
"pe_xcomet_qe": 0.984,
"pe_xcomet_errors": "[]",
# Behavioral data
"doc_num_edits": 103,
"doc_edit_order": 20,
"doc_edit_time": 118,
"doc_edit_time_filtered": 118,
"doc_keys_per_min": 52.37,
"doc_chars_per_min": 584.24,
"doc_words_per_min": 79.83,
"segment_num_edits": 9,
"segment_edit_order": 3,
"segment_edit_time": 9,
"segment_edit_time_filtered": 9,
"segment_keys_per_min": 60.0,
"segment_chars_per_min": 906.67,
"segment_words_per_min": 106.67,
"num_enter_actions": 2,
"remove_highlights": False,
# Texts and annotations
"src_text": "The speed of its emerging growth frequently outpaces the development of quality assurance and education.",
"mt_text": "De snelheid van de opkomende groei is vaak sneller dan de ontwikkeling van kwaliteitsborging en onderwijs.",
"mt_text_highlighted": "De snelheid van de opkomende groei is vaak <minor>sneller</minor> dan de ontwikkeling van kwaliteitsborging en <major>onderwijs.</major>",
"pe_text": "De snelheid van de opkomende groei is vaak sneller dan de ontwikkeling van kwaliteitsborging en opleiding.",
"mt_pe_word_aligned": "MT: De snelheid van de opkomende groei is vaak sneller dan de ontwikkeling van kwaliteitsborging en onderwijs.\n" \
"PE: De snelheid van de opkomende groei is vaak sneller dan de ontwikkeling van kwaliteitsborging en opleiding.\n" \
" S",
"mt_pe_char_aligned": "MT: De snelheid van de opkomende groei is vaak sneller dan de ontwikkeling van kwaliteitsborging en onderwijs.\n" \
"PE: De snelheid van de opkomende groei is vaak sneller dan de ontwikkeling van kwaliteitsborging en opleiding.\n" \
" SS SS SS ",
"highlights": "[{'text': 'sneller', 'severity': 'minor', 'start': 43, 'end': 50}, {'text': 'onderwijs.', 'severity': 'major', 'start': 96, 'end': 106}]"
}
```
The text is provided as-is, without further preprocessing or tokenization.
### Dataset Creation
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).
## Additional Information
### Metric signatures
The following signatures correspond to the metrics reported in the processed dataframes:
```shell
# Computed using SacreBLEU: https://github.com/mjpost/sacrebleu
BLEU: case:mixed|eff:yes|tok:13a|smooth:exp|version:2.3.1
ChrF: case:mixed|eff:yes|nc:6|nw:0|space:no|version:2.3.1
TER: case:lc|tok:tercom|norm:no|punct:yes|asian:no|version:2.3.1
# Computed using Unbabel COMET: https://github.com/Unbabel/COMET
Comet: Python3.11.9|Comet2.2.2|fp32|Unbabel/wmt22-comet-da
XComet: Python3.10.12|Comet2.2.1|fp32|Unbabel/XCOMET-XXL
```
### Dataset Curators
For problems related to this 🤗 Datasets version, please contact me at [[email protected]](mailto:[email protected]).
### Citation Information
```bibtex
TODO
``` |