Link paper to HF papers URL

README.md

@@ -1,10 +1,22 @@
 ---
+annotations_creators:
+- machine-generated
+language_creators:
+- machine-generated
+- expert-generated
 language:
 - en
 - it
 - nl
 license:
 - apache-2.0
+size_categories:
+- 10K<n<100K
+source_datasets:
+- Unbabel/TowerEval-Data-v0.1
+task_categories:
+- translation
+pretty_name: qe4pe
 tags:
 - machine-translation
 - quality-estimation
@@ -15,18 +27,6 @@ tags:
 - mqm
 - comet
 - qe
-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:
@@ -56,11 +56,11 @@ configs:
 
 # Quality Estimation for Post-Editing (QE4PE)
 
-*For more details on QE4PE, see our [paper](TBD) and our [Github repository](https://github.com/gsarti/qe4pe)*
+*For more details on QE4PE, see our [paper](https://huggingface.co/papers/2503.03044) and our [Github repository](https://github.com/gsarti/qe4pe)*
 
 ## Dataset Description
 - **Source:** [Github](https://github.com/gsarti/qe4pe)
-- **Paper:** [Arxiv](https://arxiv.org/abs/2503.03044)
+- **Paper:** [Arxiv](https://huggingface.co/papers/2503.03044)
 - **Point of Contact:** [Gabriele Sarti](mailto:[email protected])
 
 [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/)
@@ -80,7 +80,7 @@ We publicly release the granular editing logs alongside the processed dataset to
 
 ### News 📢
 
-**March 2025**: The QE4PE paper is available on [Arxiv](https://arxiv.org/abs/2503.03044).
+**March 2025**: The QE4PE paper is available on [Arxiv](https://huggingface.co/papers/2503.03044).
 
 **January 2025**: MQM annotations are now available for the `main` task.
 
@@ -229,8 +229,12 @@ A single entry in the dataframe represents a segment (~sentence) in the dataset,
 |`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). |
+|`mt_pe_word_aligned`    | Aligned visual representation of word-level edit operations (I = Insertion, D = Deletion, S = Substitution) (replace `\
+` with `
+` 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 `\
+` with `
+` 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)**|                                                                                                                       |
 |`qa_mt_annotator_id`   | Annotator ID for the MQM evaluation of `qa_mt_annotated_text`.                                                                         |
@@ -323,128 +327,4 @@ The following is an example of the subject `oracle_t1` post-editing for segment
     "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
-      }
-    ]"""
-    # QA annotations
-    "qa_mt_annotator_id": 'qa_nld_3',
-    "qa_pe_annotator_id": 'qa_nld_1',
-    "qa_mt_esa_rating": 100.0,
-    "qa_pe_esa_rating": 80.0,
-    "qa_mt_annotated_text": "De snelheid van de opkomende groei is vaak sneller dan de ontwikkeling van kwaliteitsborging en onderwijs.",
-    "qa_pe_annotated_text": "De snelheid van de opkomende groei is vaak sneller dan de ontwikkeling van kwaliteitsborging en opleiding.",
-    "qa_mt_fixed_text": "De snelheid van de opkomende groei is vaak sneller dan de ontwikkeling van kwaliteitsborging en onderwijs.",
-    "qa_pe_fixed_text": "De snelheid van de ontluikende groei overtreft vaak de ontwikkeling van kwaliteitsborging en onderwijs.",
-    "qa_mt_mqm_errors": "[]",
-    "qa_pe_mqm_errors": """[
-      {
-        "text": "opkomende",
-        "text_start": 19,
-        "text_end": 28,
-        "correction":
-        "ontluikende",
-        "correction_start": 19,
-        "correction_end": 30,
-        "description": "Mistranslation - not the correct word", 
-        "mqm_category": "Mistranslation",
-        "severity": "Minor",
-        "comment": "",
-        "edit_order": 1
-      }
-    ]"""
-
-}
-```
-
-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).
-
-### QA Annotations
-
-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.
-
-## 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
-@misc{sarti-etal-2024-qe4pe,
-      title={{QE4PE}: Word-level Quality Estimation for Human Post-Editing}, 
-      author={Gabriele Sarti and Vilém Zouhar and Grzegorz Chrupała and Ana Guerberof-Arenas and Malvina Nissim and Arianna Bisazza},
-      year={2025},
-      eprint={2503.03044},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL},
-      url={https://arxiv.org/abs/2503.03044}, 
-}
-```
+    "pe_ter_min": 0.0