metadata

annotations_creators:
  - machine-generated
  - expert-generated
language_creators:
  - found
languages:
  - en
  - it
  - vi
  - nl
  - uk
  - tr
  - ar
licenses:
  - 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

Source: Github
Paper: Arxiv
Point of Contact: Gabriele Sarti

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.
`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,
    '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}
}