---
configs:
- config_name: all
  data_files:
  - split: test
    path:
    - mtgeneval_a_F.it.tsv
    - mtgeneval_a_M.it.tsv
    - mtgeneval_a_F_st.it.tsv
    - mtgeneval_a_M_st.it.tsv
    - mtgeneval_un_F.it.tsv
    - mtgeneval_un_M.it.tsv
    - mtgeneval_a_F.es.tsv
    - mtgeneval_a_M.es.tsv
    - mtgeneval_a_F.de.tsv
    - mtgeneval_a_M.de.tsv
  default: true
- config_name: en-it
  data_files:
  - split: test
    path: 
    - mtgeneval_a_F.it.tsv
    - mtgeneval_a_M.it.tsv
    - mtgeneval_a_F_st.it.tsv
    - mtgeneval_a_M_st.it.tsv
    - mtgeneval_un_F.it.tsv
    - mtgeneval_un_M.it.tsv    
- config_name: en-es
  data_files:
  - split: test
    path:
    - mtgeneval_a_F.es.tsv
    - mtgeneval_a_M.es.tsv
- config_name: en-de
  data_files:
  - split: test
    path: 
    - mtgeneval_a_F.de.tsv
    - mtgeneval_a_M.de.tsv    
annotations_creators:
- expert-generated
license: cc-by-3.0
task_categories:
- translation
- text-generation
language:
- en
- it
- de
- es
tags:
- gender
- bias
- human-centered
- mt
- traslation
- post-edit
size_categories:
- 1K<n<10K
pretty_name: Gender Bias Post-edits
---
# 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*](https://arxiv.org/abs/2410.00545) 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 instance
- `en-it`: load the en-it portion of the dataset
- `en-es`: load the en-es portion of the dataset
- `en-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](https://aclanthology.org/2022.emnlp-main.288.pdf) corpus. 
We adapted several target references translation from the original corpus (see Appendix B.1.2 in [Savoldi et al., (2024)](https://arxiv.org/abs/2410.00545)).

⚠️ The release of the [MuST-SHE](https://aclanthology.org/2020.acl-main.619/) sentences and post-edits is temporarily suspended is temporarily suspended pending clarification of the new [policy](https://www.ted.com/about/our-organization/our-policies-terms/ted-com-terms-of-use) 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](https://github.com/amazon-science/machine-translation-gender-eval/tree/main/data/context) with unambiguous gender in the source
- mtgen_a: subset of [MTGenEval](https://github.com/amazon-science/machine-translation-gender-eval/tree/main/data/sentences/test) 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](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](https://github.com/amazon-science/machine-translation-gender-eval/blob/main/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:

```
@misc{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](https://huggingface.co/BSavoldi) for adding this dataset.