Dataset Card for gender-bias-PE data
Dataset Description
The gender-bias-PE dataset contains the post-edits and associated behavioural data of the human-centered experiments presented in the paper: What the Harm? Quantifying the Tangible Impact of Gender Bias in Machine Translation with a Human-centered Study accepted at EMNLP 2024.
The dataset allows to study the impact of gender bias in Machine Translation (MT) via human-centered measures like post-editing effort (i.e. temporal and technical). The English source sentences contained in the dataset have been automatically translated with Google Translate into Italian, Spanish, and German. 88 human participants were tasked with the light post-editing of the MT output, primarly ensure correct gender translation in the target language.
The data represent multilanguage, multiuser, and multidataset conditions. See the associated paper for full details.
Dataset Structure
Data Config
all
: load all the 3 language pairs in one single dataset instanceen-it
: load the en-it portion of the dataseten-es
: load the en-es portion of the dataseten-de
: load the en-de portion of the dataset
Source Data
The dataset is created based on a subset of parallel en-* sentences extracted from the existing MT-GenEval corpus. We adapted several target references translation from the original corpus (see Appendix B.1.2 in Savoldi et al., (2024)).
⚠️ The release of the MuST-SHE sentences and post-edits is temporarily suspended is temporarily suspended pending clarification of the new policy adopted by TED for the use of its proprietary data.
Data Fields
lang
: target language
- it
- es
- de
dataset
: original dataset
- mtgen_un: subset of MTGenEval with unambiguous gender in the source
- mtgen_a: subset of MTGenEval with ambiguous gender in the source
user_type
: type of user that carried out the post-edit
- professional: experienced translator
- student: unexperienced user
original_id
: unique sentence identifier from the original dataset
gender
: gender expressed in the original reference translation and in the post-edited sentence
- F: feminine
- M: masculine
segment
: source English sentence
tgt
: target reference translation
raw_word_count
: number of source words
time_to_edit
: time to edit in milliseconds
suggestion
: Google Translate output
secs_per_word
: time to edit per source word
parsed_time_to_edit
: time to edit as duration
last_translation
: post-edited output
HTER
: sentence-level HTER score
Annotation
The post-edits were carried out following dedicated guidelines, which are available at https://github.com/bsavoldi/post-edit_guidelines
License
The MTGenvEval-based portion of the dataset is released with the same CC-BY-SA-3.0 license as the original corpus.
Citation
The dataset is associated with a paper accepted at EMNLP 2024. Please cite the paper when referencing the gender-bias-pe corpus as:
@inproceedings{savoldi2024whattheharm,
title={{What the Harm? Quantifying the Tangible Impact of Gender Bias in Machine Translation with a Human-centered Study}},
author={Savoldi, Beatrice and
Papi, Sara and
Negri, Matteo and
Guerberof, Ana and
Bentivogli, Luisa},
year={2024},
booktitle = {Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing},
month = {nov},
year = {2023},
address = {Miami},
publisher = {Association for Computational Linguistics}
}
Contribution
Thanks to @Bsavoldi for adding this dataset.
- Downloads last month
- 85