Updated new version

README.md

@@ -11,7 +11,7 @@ licenses:
 - private
 multilinguality:
 - translation
-pretty_name: htstyle-iknlp2022
+pretty_name: iknlp22-pestyle
 size_categories:
 - 1K<n<10K
 source_datasets:
@@ -20,15 +20,14 @@ task_categories:
 - translation
 ---
 
-# Dataset Card for IK-NLP-22 Translator Stylometry
+# Dataset Card for IK-NLP-22 Project 1: A Study in Post-Editing Stylometry
 
 ## Table of Contents
 
-- [Dataset Card for IK-NLP-22 Translator Stylometry](#dataset-card-for-ik-nlp-22-translator-stylometry)
+- [Dataset Card for IK-NLP-22 Project 1: A Study in Post-Editing Stylometry](#dataset-card-for-ik-nlp-22-project-1-a-study-in-post-editing-stylometry)
   - [Table of Contents](#table-of-contents)
   - [Dataset Description](#dataset-description)
     - [Dataset Summary](#dataset-summary)
-    - [Projects](#projects)
     - [Languages](#languages)
   - [Dataset Structure](#dataset-structure)
     - [Data Instances](#data-instances)
@@ -51,13 +50,9 @@ task_categories:
 
 This dataset contains a sample of sentences taken from the [FLORES-101](https://huggingface.co/datasets/gsarti/flores_101) dataset that were either translated from scratch or post-edited from an existing automatic translation by three human translators. Translation were performed for the English-Italian language pair, and translators' behavioral data (keystrokes, pauses, editing times) were collected using the [PET](https://github.com/wilkeraziz/PET) platform.
 
-This dataset is made available for final projects of the 2022 edition of the Natural Language Processing course at the [Information Science Master's Degree](https://www.rug.nl/masters/information-science/?lang=en) at the University of Groningen, taught by [Arianna Bisazza](https://research.rug.nl/en/persons/arianna-bisazza) with the assistance of [Gabriele Sarti](https://research.rug.nl/en/persons/gabriele-sarti).
+This dataset is made available for final projects of the 2022 edition of the Natural Language Processing course at the [Information Science Master's Degree](https://www.rug.nl/masters/information-science/?lang=en) at the University of Groningen, taught by [Arianna Bisazza](https://research.rug.nl/en/persons/arianna-bisazza) with the assistance of [Gabriele Sarti](https://research.rug.nl/en/persons/gabriele-sarti) and [Anjali Nair](https://nl.linkedin.com/in/anjalinair012).
 
-**Disclaimer**: *This repository is provided without direct data access due to currently unpublished results.* _**For this reason, it is strictly forbidden to share or publish all the data associated to this repository**_ *Students will be provided with a compressed folder containing the data upon choosing a project based on this dataset. To load the dataset using 🤗 Datasets, download and unzip the provided folder and pass it to the* `load_dataset` *method as:* `datasets.load_dataset('GroNLP/ik-nlp-22_htstyle', 'main', data_dir='path/to/unzipped/folder')`
-
-### Projects
-
-To be provided.
+**Disclaimer**: *This repository is provided without direct data access due to currently unpublished results.* _**For this reason, it is strictly forbidden to share or publish all the data associated to this repository**_ *Students will be provided with a compressed folder containing the data upon choosing a project based on this dataset. To load the dataset using 🤗 Datasets, download and unzip the provided folder and pass it to the* `load_dataset` *method as:* `datasets.load_dataset('GroNLP/ik-nlp-22_pestyle', 'main', data_dir='path/to/unzipped/folder')`
 
 ### Languages
 
@@ -67,24 +62,20 @@ The language data of is in English (BCP-47 `en`) and Italian (BCP-47 `it`)
 
 ### Data Instances
 
-The dataset contains a single configuration, `main`, with two data splits: `train` and `test`.
+The dataset contains a single configuration, `main`, with four data splits: the main `train` split in which all fields are available, and three test splits: `test_mask_subject`, `test_mask_modality`, `test_mask_time`. See more details in the [Data Splits](#data-splits) section.
 
 ### Data Fields
 
-The following fields are contained in the dataset:
+The following fields are contained in the training set:
 
 |Field|Description|
 |-----|-----------|
-|`item`   | The sentence identifier. The first digits of the number represent the document containing the sentence, while the last digit of the number represents the sentence position inside the document. Documents can contain from 3 to 5 semantically-related sentences each. |
-|`subject`   | The identifier for the translator performing the translation from scratch or post-editing task. Values: `t1`, `t2` or `t3`. |
-|`tasktype`   | The setting of the translation task. Values: `ht` (translation from scratch), `pe1` (post-editing Google Translate translations), `pe2` (post-editing [mBART](https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt) translations). |
-|`sl_text`   | The original source sentence extracted from Wikinews, wikibooks or wikivoyage. |
+|`item_id`   | The sentence identifier. The first digits of the number represent the document containing the sentence, while the last digit of the number represents the sentence position inside the document. Documents can contain from 3 to 5 semantically-related sentences each. |
+|`subject_id`   | The identifier for the translator performing the translation from scratch or post-editing task. Values: `t1`, `t2` or `t3`. |
+|`modality`   | The modality of the translation task. Values: `ht` (translation from scratch), `pe1` (post-editing Google Translate translations), `pe2` (post-editing [mBART](https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt) translations). |
+|`src_text`   | The original source sentence extracted from Wikinews, wikibooks or wikivoyage. |
 |`mt_text`   | Missing if tasktype is `ht`. Otherwise, contains the automatically-translated sentence before post-editing. |
-|`tl_text`   | Final sentence produced by the translator (either via translation from scratch of `sl_text` or post-editing `mt_text`) |
-|`len_sl_chr`   | Length of the original source text in characters. |
-|`len_tl_chr`   | Length of the final translated text in characters. |
-|`len_sl_wrd`   | Length of the original source text in words. |
-|`len_tl_wrd`   | Length of the final translated text in words. |
+|`tgt_text`   | Final sentence produced by the translator (either via translation from scratch of `sl_text` or post-editing `mt_text`) |
 |`edit_time`   | Total editing time for the translation in seconds. |
 |`k_total`   | Total number of keystrokes for the translation. |
 |`k_letter`   | Total number of letter keystrokes for the translation. |
@@ -96,41 +87,39 @@ The following fields are contained in the dataset:
 |`k_copy`   | Total number of copy (Ctrl + C) actions during the translation. |
 |`k_cut`   | Total number of cut (Ctrl + X) actions during the translation. |
 |`k_paste`   | Total number of paste (Ctrl + V) actions during the translation. |
-|`np_300`   | Number of pauses of 300ms or more during the translation. |
-|`lp_300`   | Total duration of pauses of 300ms or more, in milliseconds. |
-|`np_1000`   | Number of pauses of 1s or more during the translation. |
-|`lp_1000`   | Total duration of pauses of 1000ms or more, in milliseconds. |
-|`mt_tl_bleu`   | Sentence-level BLEU score between MT and post-edited fields (empty for tasktype `ht`) computed using the [SacreBLEU](https://github.com/mjpost/sacrebleu) library with default parameters. |
-|`mt_tl_chrf`   | Sentence-level chrF score between MT and post-edited fields (empty for tasktype `ht`) computed using the [SacreBLEU](https://github.com/mjpost/sacrebleu) library with default parameters. |
-|`mt_tl_Ins`   | Number of post-editing insertions (empty for tasktype `ht`) computed using the [tercom](https://github.com/jhclark/tercom) library. |
-|`mt_tl_Del`   | Number of post-editing deletions (empty for tasktype `ht`) computed using the [tercom](https://github.com/jhclark/tercom) library. |
-|`mt_tl_Sub`   | Number of post-editing substitutions (empty for tasktype `ht`) computed using the [tercom](https://github.com/jhclark/tercom) library. |
-|`mt_tl_Shft`   | Number of post-editing shifts (empty for tasktype `ht`) computed using the [tercom](https://github.com/jhclark/tercom) library. |
-|`mt_tl_ter`   | Sentence-level TER score between MT and post-edited fields (empty for tasktype `ht`) computed using the [tercom](https://github.com/jhclark/tercom) library. |
-|`mt_tl_edits`   | Aligned visual representation of REF (`mt_text`), HYP (`tl_text`) and edit operations (I = Insertion, D = Deletion, S = Shift or Substitution) performed on the field. Replace `:::` with `\n` to show aligned.|
+|`n_pause_geq_300`   | Number of pauses of 300ms or more during the translation. |
+|`len_pause_geq_300`   | Total duration of pauses of 300ms or more, in milliseconds. |
+|`n_pause_geq_1000`   | Number of pauses of 1s or more during the translation. |
+|`len_pause_geq_1000`   | Total duration of pauses of 1000ms or more, in milliseconds. |
+|`num_annotations` | Number of times the translator focused the texbox for performing the translation of the sentence during the translation session. E.g. 1 means the translation was performed once and never revised. |
+|`n_insert`   | Number of post-editing insertions (empty for modality `ht`) computed using the [tercom](https://github.com/jhclark/tercom) library. |
+|`n_delete`   | Number of post-editing deletions (empty for modality `ht`) computed using the [tercom](https://github.com/jhclark/tercom) library. |
+|`n_substitute`   | Number of post-editing substitutions (empty for modality `ht`) computed using the [tercom](https://github.com/jhclark/tercom) library. |
+|`n_shift`   | Number of post-editing shifts (empty for modality `ht`) computed using the [tercom](https://github.com/jhclark/tercom) library. |
+|`bleu`   | Sentence-level BLEU score between MT and post-edited fields (empty for modality `ht`) computed using the [SacreBLEU](https://github.com/mjpost/sacrebleu) library with default parameters. |
+|`chrf`   | Sentence-level chrF score between MT and post-edited fields (empty for modality `ht`) computed using the [SacreBLEU](https://github.com/mjpost/sacrebleu) library with default parameters. |
+
+|`ter`   | Sentence-level TER score between MT and post-edited fields (empty for modality `ht`) computed using the [tercom](https://github.com/jhclark/tercom) library. |
+|`aligned_edit`   | Aligned visual representation of REF (`mt_text`), HYP (`tl_text`) and edit operations (I = Insertion, D = Deletion, S = Substitution) performed on the field. Replace `\\n` with `\n` to show the three aligned rows.|
 
 ### Data Splits
 
 | config| train| test|
 |------:|-----:|----:|
-|`main` | 1159 | 107 |
+|`main` | 1170 | 120 |
 
 #### Train Split
 
-The `train` split contains a total of 1159 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 `t3` post-editing a machine translation produced by system 2 (tasktype `pe2`) taken from the `train` split. The field `mt_tl_edits` is showed over three lines to provide a visual understanding of its contents.
+The `train` split contains a total of 1170 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 `t3` post-editing a machine translation produced by system 2 (tasktype `pe2`) taken from the `train` split. The field `aligned_edit` is showed over three lines to provide a visual understanding of its contents.
 
 ```json
 {
-    "item": 1072,
-    "subject": "t3",
+    "item_id": 1072,
+    "subject_id": "t3",
     "tasktype": "pe2",
-    "sl_text": "At the beginning dress was heavily influenced by the Byzantine culture in the east.",
+    "src_text": "At the beginning dress was heavily influenced by the Byzantine culture in the east.",
     "mt_text": "All'inizio il vestito era fortemente influenzato dalla cultura bizantina dell'est.",
-    "tl_text": "Inizialmente, l'abbigliamento era fortemente influenzato dalla cultura bizantina orientale.",
-    "len_sl_chr": 83,
-    "len_tl_chr": 91,
-    "len_sl_wrd": 14,
-    "len_tl_wrd": 9,
+    "tgt+text": "Inizialmente, l'abbigliamento era fortemente influenzato dalla cultura bizantina orientale.",
     "edit_time": 45.687,
     "k_total": 51,
     "k_letter": 31,
@@ -142,19 +131,20 @@ The `train` split contains a total of 1159 triplets (or pairs, when translation
     "k_copy": 0,
     "k_cut": 0,
     "k_paste": 0,
-    "np_300": 9,
-    "lp_300": 40032,
-    "np_1000": 5,
-    "lp_1000": 38392,
-    "mt_tl_bleu": 47.99,
-    "mt_tl_chrf": 62.05,
-    "mt_tl_Ins": 0.0,
-    "mt_tl_Del": 1.0,
-    "mt_tl_Sub": 3.0,
-    "mt_tl_Shft": 0.0,
-    "mt_tl_ter": 40.0,
-    "mt_tl_edits: "REF:  all'inizio il            vestito         era fortemente influenzato dalla cultura bizantina dell'est.:::
-                   HYP:  ********** inizialmente, l'abbigliamento era fortemente influenzato dalla cultura bizantina orientale.::: 
+    "n_pause_geq_300": 9,
+    "len_pause_geq_300": 40032,
+    "n_pause_geq_1000": 5,
+    "len_pause_geq_1000": 38392,
+    "num_annotations": 1,
+    "n_insert": 0.0,
+    "n_delete": 1.0,
+    "n_substitute": 3.0,
+    "n_shift": 0.0,
+    "bleu": 47.99,
+    "chrf": 62.05,
+    "ter": 40.0,
+    "aligned_edit: "REF:  all'inizio il            vestito         era fortemente influenzato dalla cultura bizantina dell'est.\\n
+                   HYP:  ********** inizialmente, l'abbigliamento era fortemente influenzato dalla cultura bizantina orientale.\\n 
                    EVAL: D          S             S                                                                  S"
 }
 ```
@@ -163,17 +153,17 @@ The text is provided as-is, without further preprocessing or tokenization.
 
 #### Test split
 
-The `test` split contains 107 entries following the same structure as `train`, with few omissions:
+The three `test` splits contains the same 120 entries each, following the same structure as `train`. Each test split omit some of the fields to prevent leakage of information:
 
-- the `subject` field was set to `nan` for the translator stylometry task.
+- In `test_mask_subject` the `subject_id` is absent, for the main task of post-editor stylometry.
 
-- the `tasktype`, `mt_text` and `mt_tl` evaluation metrics fields were set to `nan` for the translation setting prediction task.
+- In `test_mask_modality` the following fields are absent for the modality prediction extra task: `modality`, `mt_text`, `n_insert`, `n_delete`, `n_substitute`, `n_shift`, `ter`, `bleu`, `chrf`, `aligned_edit`.
 
-- the `edit_time`, `lp_300` and `lp_1000` fields were set to -1 for the translation time prediction task.
+- In `test_mask_time` the following fields are absent for the time and pause prediction extra task: `edit_time`, `n_pause_geq_300`, `len_pause_geq_300`, `n_pause_geq_1000`, and `len_pause_geq_1000`.
 
 ### Dataset Creation
 
-The dataset was parsed from PET XML files into CSV format using the scripts by [Antonio Toral](https://research.rug.nl/en/persons/antonio-toral) found at the following link: [https://github.com/antot/postediting_novel_frontiers](https://github.com/antot/postediting_novel_frontiers)
+The dataset was parsed from PET XML files into CSV format using a script adapted from the one by [Antonio Toral](https://research.rug.nl/en/persons/antonio-toral) found at the following link: [https://github.com/antot/postediting_novel_frontiers](https://github.com/antot/postediting_novel_frontiers)
 
 ## Additional Information
 

ik-nlp-22_htstyle.py → ik-nlp-22_pestyle.py

@@ -17,12 +17,39 @@ _HOMEPAGE = "https://www.rug.nl/masters/information-science/?lang=en"
 _LICENSE = "Sharing and publishing of the data is not allowed at the moment."
 
 _SPLITS = {
-    "train": os.path.join("IK_NLP_22_HTSTYLE", "train.csv"),
-    "test": os.path.join("IK_NLP_22_HTSTYLE", "test.csv")
+    "train": os.path.join("IK_NLP_22_PESTYLE", "train.tsv"),
+    "test_mask_subject": os.path.join("IK_NLP_22_PESTYLE", "test.tsv"),
+    "test_mask_modality": os.path.join("IK_NLP_22_PESTYLE", "test.tsv"),
+    "test_mask_time": os.path.join("IK_NLP_22_PESTYLE", "test.tsv")
 }
 
+_ALL_FIELDS = [
+    "item_id", "subject_id", "modality",
+    "src_text", "mt_text", "tgt_text",
+    "edit_time", "k_total", "k_letter", "k_digit", "k_white", "k_symbol", "k_nav", "k_erase", 
+    "k_copy", "k_cut", "k_paste", "n_pause_geq_300", "len_pause_geq_300", 
+    "n_pause_geq_1000", "len_pause_geq_1000", "num_annotations",
+    "n_insert", "n_delete", "n_substitute", "n_shift", "bleu", "chrf", "ter", "aligned_edit"
+]
+
+_FIELDS_MASK_SUBJECT = [f for f in _ALL_FIELDS if f not in ["subject_id"]]
+_FIELDS_MASK_MODALITY = [f for f in _ALL_FIELDS if f not in [
+    "modality", "mt_text", "n_insert", "n_delete", "n_substitute", 
+    "n_shift", "ter", "bleu", "chrf", "aligned_edit"
+]]
+_FIELDS_MASK_TIME = [f for f in _ALL_FIELDS if f not in [
+    "edit_time", "n_pause_geq_300", "len_pause_geq_300", 
+    "n_pause_geq_1000", "len_pause_geq_1000"
+]]
+
+_DICT_FIELDS = {
+    "train": _ALL_FIELDS,
+    "test_mask_subject": _FIELDS_MASK_SUBJECT,
+    "test_mask_modality": _FIELDS_MASK_MODALITY,
+    "test_mask_time": _FIELDS_MASK_TIME
+}
 
-class IkNlp22HtStyleConfig(datasets.BuilderConfig):
+class IkNlp22PEStyleConfig(datasets.BuilderConfig):
     """BuilderConfig for the IK NLP '22 HT-Style Dataset."""
 
     def __init__(
@@ -40,42 +67,13 @@ class IkNlp22HtStyleConfig(datasets.BuilderConfig):
         self.features = features
 
 
-class IkNlp22HtStyle(datasets.GeneratorBasedBuilder):
+class IkNlp22PEStyle(datasets.GeneratorBasedBuilder):
     VERSION = datasets.Version("1.0.0")
 
     BUILDER_CONFIGS = [
-        IkNlp22HtStyleConfig(
+        IkNlp22PEStyleConfig(
             name="main",
-            features=[
-                "item",
-                "subject",
-                "tasktype",
-                "sl_text",
-                "mt_text",
-                "tl_text",
-                "len_sl_chr",
-                "len_tl_chr",
-                "len_sl_wrd",
-                "len_tl_wrd",
-                "edit_time",
-                "k_total",
-                "k_letter",
-                "k_digit",
-                "k_white",
-                "k_symbol",
-                "k_nav",
-                "k_erase",
-                "k_copy",
-                "k_cut",
-                "k_paste",
-                "np_300",
-                "lp_300",
-                "np_1000",
-                "lp_1000",
-                "mt_tl_bleu",
-                "mt_tl_chrf",
-                "mt_tl_ter",
-            ],
+            features=_ALL_FIELDS,
         ),
     ]
 
@@ -86,22 +84,23 @@ class IkNlp22HtStyle(datasets.GeneratorBasedBuilder):
     def manual_download_instructions(self):
         return (
             "The access to the data is restricted to students of the IK MSc NLP 2022 course working on a related project."
-            "To load the data using this dataset, download and extract the IK_NLP_22_HTSTYLE folder you were provided upon selecting the final project."
-            "After extracting it, the folder (referred to as root) must contain a IK_NLP_22_HTSTYLE subfolder, containing train.csv and test.csv files."
-            "Then, load the dataset with: `datasets.load_dataset('GroNLP/ik-nlp-22_htstyle', 'main', data_dir='path/to/root/folder')`"
+            "To load the data using this dataset, download and extract the IK_NLP_22_PESTYLE folder you were provided upon selecting the final project."
+            "After extracting it, the folder (referred to as root) must contain a IK_NLP_22_PESTYLE subfolder, containing train.csv and test.csv files."
+            "Then, load the dataset with: `datasets.load_dataset('GroNLP/ik-nlp-22_pestyle', 'main', data_dir='path/to/root/folder')`"
         )
 
     def _info(self):
         features = {feature: datasets.Value("int32") for feature in self.config.features}
-        features["subject"] = datasets.Value("string")
-        features["tasktype"] = datasets.Value("string")
-        features["sl_text"] = datasets.Value("string")
+        features["subject_id"] = datasets.Value("string")
+        features["modality"] = datasets.Value("string")
+        features["src_text"] = datasets.Value("string")
         features["mt_text"] = datasets.Value("string")
-        features["tl_text"] = datasets.Value("string")
+        features["tgt_text"] = datasets.Value("string")
+        features["aligned_edit"] = datasets.Value("string")
         features["edit_time"] = datasets.Value("float32")
-        features["mt_tl_bleu"] = datasets.Value("float32")
-        features["mt_tl_chrf"] = datasets.Value("float32")
-        features["mt_tl_ter"] = datasets.Value("float32")
+        features["bleu"] = datasets.Value("float32")
+        features["chrf"] = datasets.Value("float32")
+        features["ter"] = datasets.Value("float32")
         return datasets.DatasetInfo(
             description=_DESCRIPTION,
             features=datasets.Features(features),
@@ -115,30 +114,27 @@ class IkNlp22HtStyle(datasets.GeneratorBasedBuilder):
         data_dir = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))
         if not os.path.exists(data_dir):
             raise FileNotFoundError(
-                "{} does not exist. Make sure you insert the unzipped IK_NLP_22_HTSTYLE dir via "
-                "`datasets.load_dataset('GroNLP/ik-nlp-22_htstyle', data_dir=...)`"
+                "{} does not exist. Make sure you insert the unzipped IK_NLP_22_PESTYLE dir via "
+                "`datasets.load_dataset('GroNLP/ik-nlp-22_pestyle', data_dir=...)`"
                 "Manual download instructions: {}".format(
                     data_dir, self.manual_download_instructions
                 )
             )
         return [
             datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
+                name=name,
                 gen_kwargs={
-                    "filepath": os.path.join(data_dir, _SPLITS["train"]),
+                    "filepath": os.path.join(data_dir, path),
+                    "fields": _DICT_FIELDS[name],
                 },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir, _SPLITS["test"]),
-                },
-            ),
+            )
+            for name, path in _SPLITS.items()
         ]
     
-    def _generate_examples(self, filepath: str):
+    def _generate_examples(self, filepath: str, fields):
         """Yields examples as (key, example) tuples."""
         data = pd.read_csv(filepath)
+        data = data[fields]
         print(data.shape)
         for id_, row in data.iterrows():
             yield id_, row.to_dict()