--- 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 >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. | ### 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 sneller dan de ontwikkeling van kwaliteitsborging en onderwijs.", "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 [gabriele.sarti996@gmail.com](mailto:gabriele.sarti996@gmail.com). ### Citation Information ```bibtex TODO ```