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GroNLP
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ik-nlp-22_pestyle

Tasks:
Translation
Languages:
English
Italian
Size:
1K<n<10K
License:
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metadata
annotations_creators:
  - machine-generated
  - expert-generated
language_creators:
  - found
languages:
  - en
  - it
licenses:
  - private
multilinguality:
  - translation
pretty_name: htstyle-iknlp2022
size_categories:
  - 1K<n<10K
source_datasets:
  - original
task_categories:
  - translation

Dataset Card for IK-NLP-22 Translator Stylometry

Table of Contents

Dataset Description

Dataset Summary

This dataset contains a sample of sentences taken from the 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 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 at the University of Groningen, taught by Arianna Bisazza with the assistance of Gabriele Sarti.

Disclaimer: This repository is provided without direct data access due to currently unpublished results.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.

Languages

The language data of is in English (BCP-47 en) and Italian (BCP-47 it)

Dataset Structure

Data Instances

The dataset contains a single configuration, main, with two data splits: train and test.

Data Fields

The following fields are contained in the dataset:

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 translations).
sl_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.
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.
k_digit Total number of digit keystrokes for the translation.
k_white Total number of whitespace keystrokes for the translation.
k_symbol Total number of symbol (punctuation, etc.) keystrokes for the translation.
k_nav Total number of navigation keystrokes (left-right arrows, mouse clicks) for the translation.
k_erase Total number of erase keystrokes (backspace, cancel) for the translation.
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 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 library with default parameters.
mt_tl_Ins Number of post-editing insertions (empty for tasktype ht) computed using the tercom library.
mt_tl_Del Number of post-editing deletions (empty for tasktype ht) computed using the tercom library.
mt_tl_Sub Number of post-editing substitutions (empty for tasktype ht) computed using the tercom library.
mt_tl_Shft Number of post-editing shifts (empty for tasktype ht) computed using the tercom library.
mt_tl_ter Sentence-level TER score between MT and post-edited fields (empty for tasktype ht) computed using the 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.

Data Splits

config train test
main 1159 107

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.

{
    "item": 1072,
    "subject": "t3",
    "tasktype": "pe2",
    "sl_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,
    "edit_time": 45.687,
    "k_total": 51,
    "k_letter": 31,
    "k_digit": 0,
    "k_white": 2,
    "k_symbol": 3,
    "k_nav": 7,
    "k_erase": 3,
    "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.::: 
                   EVAL: D          S             S                                                                  S"
}

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 subject field was set to nan for the translator stylometry task.

  • the tasktype, mt_text and mt_tl evaluation metrics fields were set to nan for the translation setting prediction task.

  • the edit_time, lp_300 and lp_1000 fields were set to -1 for the translation time prediction task.

Dataset Creation

The dataset was parsed from PET XML files into CSV format using the scripts by Antonio Toral found at the following link: https://github.com/antot/postediting_novel_frontiers

Additional Information

Dataset Curators

For problems related to this 🤗 Datasets version, please contact us at [email protected].

Licensing Information

It is forbidden to share or publish the data associated with this 🤗 Dataset version.

Citation Information

No citation information is provided for this dataset.