Hugging Face's logo

Datasets:
GroNLP
/
divemt

Tasks:
Translation
Languages: English Italian Vietnamese
Multilinguality: translation
Size Categories: 1K<n<10K
Language Creators: found
Annotations Creators: machine-generated expert-generated
Source Datasets: original
ArXiv:
Tags:
License:
Dataset card Viewer Files Community
1
divemt / README.md
julien-c's picture
julien-c HF staff
Fix `license` metadata
80b3d08
preview code
|
raw history blame
No virus
11.6 kB
metadata
annotations_creators:
  - machine-generated
  - expert-generated
language_creators:
  - found
language:
  - en
  - it
  - vi
  - nl
  - uk
  - tr
  - ar
license:
  - gpl-3.0
multilinguality:
  - translation
pretty_name: divemt
size_categories:
  - 1K<n<10K
source_datasets:
  - original
task_categories:
  - translation

Dataset Card for DivEMT

Dataset Description

DivEMT Visualization

For an overview of DivEMT, see our Paper and our Github repository

Dataset Summary

This dataset contains the processed warmup and main splits of the DivEMT dataset. A sample of documents extracted from the Flores-101 corpus were either translated from scratch or post-edited from an existing automatic translation by a total of 18 professional translators across six typologically diverse languages (Arabic, Dutch, Italian, Turkish, Ukrainian, Vietnamese). During the translation, behavioral data (keystrokes, pauses, editing times) were collected using the PET platform.

We publicly release the processed dataset including all collected behavioural data, to foster new research on the ability of state-of-the-art NMT systems to generate text in typologically diverse languages.

Languages

The language data of DivEMT is in English (BCP-47 en), Italian (BCP-47 it), Dutch (BCP-47 nl), Arabic (BCP-47 ar), Turkish (BCP-47 tr), Ukrainian (BCP-47 uk) and Vietnamese (BCP-47 vi)

Dataset Structure

Data Instances

The dataset contains two configurations: main and warmup. main contains the full data collected during the main task and analyzed during our experiments. warmup contains the data collected in the verification phase, before the main task begins.

Data Fields

The following fields are contained in the training set:

Field Description
unit_id The full entry identifier. Format: flores101-{config}-{lang}-{doc_id}-{modality}-{sent_num}
flores_id Index of the sentence in the original Flores-101 dataset
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.
task_type The modality of the translation task. Values: ht (translation from scratch), pe1 (post-editing Google Translate translations), pe2 (post-editing mBART translations).
translation_type Either ht for from scratch or pe for post-editing
src_len_chr Length of the English source text in number of characters
mt_len_chr Length of the machine translation in number of characters (NaN for ht)
tgt_len_chr Length of the target text in number of characters
src_len_wrd Length of the English source text in number of words
mt_len_wrd Length of the machine translation in number of words (NaN for ht)
tgt_len_wrd Length of the target text in number of 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.
k_do Total number of Enter actions during the translation.
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.
event_time Total time summed across all translation events, should be comparable to edit_time
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 library.
n_delete Number of post-editing deletions (empty for modality ht) computed using the tercom library.
n_substitute Number of post-editing substitutions (empty for modality ht) computed using the tercom library.
n_shift Number of post-editing shifts (empty for modality ht) computed using the tercom library.
tot_shifted_words Total amount of shifted words from all shifts present in the sentence.
tot_edits Total of all edit types for the sentence.
hter Human-mediated Translation Edit Rate score computed between the MT and post-edited outputs using the tercom library.
cer Character-level HTER score computed between the MT and post-edited outputs using the CharacTER library.
bleu Sentence-level BLEU score between MT and post-edited fields (empty for modality ht) computed using the SacreBLEU library with default parameters.
chrf Sentence-level chrF score between MT and post-edited fields (empty for modality ht) computed using the SacreBLEU library with default parameters.
lang_id Language identifier for the sentence
doc_id Document identifier for the sentence
time_s Edit time expressed in seconds. time_m and time_h also available for minutes and hours respectively.
time_per_char Edit time per source character, expressed in seconds. Also available as time_per_word.
key_per_char Proportion of keys per character needed to perform the translation.
words_per_hour Amount of source words translated or post-edited per hour. Also available as words_per_minute.
per_subject_visit_order Id denoting the order in which the translator accessed documents. 1 correspond to the first accessed document.
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.
tgt_text Final sentence produced by the translator (either via translation from scratch of sl_text or post-editing mt_text)
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
main 7740 (107 docs i.e. 430 sents x 18 translators)
warmup 360 (5 docs i.e. 20 sents x 18 translators)

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 t1 post-editing a machine translation produced by Google Translate (task_type pe1) taken from the train split for Turkish. The field aligned_edit is showed over three lines to provide a visual understanding of its contents.

{
    'unit_id': 'flores101-main-tur-46-pe1-3',
    'flores_id': 871,
    'item_id': 'flores101-main-463',
    'subject_id': 'tur_t1',
    'task_type': 'pe1',
    'translation_type': 'pe',
    'src_len_chr': 109,
    'mt_len_chr': 129.0,
    'tgt_len_chr': 120,
    'src_len_wrd': 17,
    'mt_len_wrd': 15.0,
    'tgt_len_wrd': 13,
    'edit_time': 11.762999534606934,
    'k_total': 31,
    'k_letter': 9,
    'k_digit': 0,
    'k_white': 0,
    'k_symbol': 0,
    'k_nav': 20,
    'k_erase': 2,
    'k_copy': 0,
    'k_cut': 0,
    'k_paste': 0,
    'k_do': 0,
    'n_pause_geq_300': 2,
    'len_pause_geq_300': 4986,
    'n_pause_geq_1000': 1,
    'len_pause_geq_1000': 4490,
    'event_time': 11763,
    'num_annotations': 2,
    'last_modification_time': 1643569484,
    'n_insert': 0.0,
    'n_delete': 2.0,
    'n_substitute': 1.0,
    'n_shift': 0.0,
    'tot_shifted_words': 0.0,
    'tot_edits': 3.0,
    'hter': 20.0,
    'cer': 0.10,
    'bleu': 0.0,
    'chrf': 2.569999933242798,
    'lang_id': 'tur',
    'doc_id': 46,
    'time_s': 11.762999534606934,
    'time_m': 0.1960500031709671,
    'time_h': 0.0032675000838935375,
    'time_per_char': 0.1079174280166626,
    'time_per_word': 0.6919412016868591,
    'key_per_char': 0.2844036817550659,
    'words_per_hour': 5202.75439453125,
    'words_per_minute': 86.71257019042969,
    'per_subject_visit_order': 201,
    'src_text': 'As one example, American citizens in the Middle East might face different situations from Europeans or Arabs.',
    'mt_text': "Bir örnek olarak, Orta Doğu'daki Amerikan vatandaşları, Avrupalılardan veya Araplardan farklı durumlarla karşı karşıya kalabilir.",
    'tgt_text': "Örneğin, Orta Doğu'daki Amerikan vatandaşları, Avrupalılardan veya Araplardan farklı durumlarla karşı karşıya kalabilir.",
    'aligned_edit': "REF:  bir örnek olarak,  orta doğu'daki amerikan vatandaşları, avrupalılardan veya araplardan farklı durumlarla karşı karşıya kalabilir.\\n
                     HYP:  *** ***** örneğin, orta doğu'daki amerikan vatandaşları, avrupalılardan veya araplardan farklı durumlarla karşı karşıya kalabilir.\\n
                     EVAL: D   D     S"
}

The text is provided as-is, without further preprocessing or tokenization.

Dataset Creation

The dataset was parsed from PET XML files into CSV format using a script adapted from the one 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 me at g.sarti@rug.nl.

Citation Information 

@article{sarti-etal-2022-divemt,
    title={{DivEMT}: Neural Machine Translation Post-Editing Effort Across Typologically Diverse Languages},
    author={Sarti, Gabriele and Bisazza, Arianna and Guerberof Arenas, Ana and Toral, Antonio},
    journal={ArXiv preprint 2205.12215},
    url={https://arxiv.org/abs/2205.12215},
    year={2022},
    month={may}
}