README.md

---
configs:
- config_name: mGeNTE
  data_files:
  - split: test
    path:
    - "en-es.tsv"
    - "en-de.tsv"
    - "en-it.tsv"  
    - "en-el.tsv"
  default: true
- config_name: mGeNTE en-it
  data_files:
  - split: test
    path: 
    - "en-it.tsv" 
- config_name: mGeNTE en-es
  data_files:
  - split: test
    path: 
    - "en-es.tsv" 
- config_name: mGeNTE en-de
  data_files:
  - split: test
    path: 
    - "en-de.tsv" 
- config_name: mGeNTE en-el
  data_files:
  - split: test
    path: 
    - "en-el.tsv" 
- config_name: mGeNTE_common
  data_files:
  - split: test
    path: 
    - "en-es_common.tsv"
    - "en-de_common.tsv"
    - "en-it_common.tsv"
    - "en-el_common.tsv"
annotations_creators:
- expert-generated
language:
- en
- it
- de
- es
- el
language_creators:
- expert-generated
license:
- cc-by-4.0
multilinguality:
- multilingual
- translation
paperswithcode_id: null
pretty_name: MGeNTE
size_categories:
- 1K<n<10K
source_datasets: []
tags:
- gender
- bias
- inclusivity
- rewriting
- translation
- mt
task_categories:
- translation
- text-generation
task_ids:
- language-modeling
---



# Dataset Card for mGeNTE

**Homepage:** https://mt.fbk.eu/mgente/

**Code:** https://github.com/g8a9/mgente-gap

The mGeNTE dataset is introduced in the paper "[Mind the Inclusivity Gap: Multilingual Gender-Neutral Translation Evaluation with mGeNTE](https://arxiv.org/abs/2501.09409v3)", presented at [EMNLP 2025](https://2025.emnlp.org/). 

## Dataset Summary
mGeNTE (**M**ultilingual **Ge**nder-**N**eutral **T**ranslation **E**valuation) is a natural, multilingual corpus designed to benchmark gender-neutral language and automatic translation.

mGente is built upon European Parliament speech data extracted from the [Europarl corpus](https://www.statmt.org/europarl/archives.html), and represents a multilingual expansion of the bilingual [GeNTE](https://huggingface.co/datasets/FBK-MT/GeNTE) dataset (v1.0, now superseded by mGeNTE). 

For each language pair, mGeNTE comprises 1,500 parallel sentences (tot. 6,000 entries), which are enriched with manual annotations and feature a balanced distribution of translation phenomena that either entail i) a gender-neutral translation (`set-N`), or ii) a gendered translation in the target language (`set-G`).


### Supported Tasks and Languages

mGeNTE supports cross-lingual (en-it, en-es, en-de, en-el) gender inclusive translation and intra-lingual (it-it, es-es, de-de, el-el) gender inclusive rewriting tasks.

## Dataset Structure

### Data Instances

The dataset consists of two configuration types: 

- **`mGeNTE`:** The complete mGeNTE corpus and its annotations, consisting of a tsv file for each language pair
-  **`mGeNTE_common`:** Subset of the mGeNTE corpus that comprises three alternative gender-neutral reference translations


### Data Fields

Each tsv file in **`mGeNTE`** is organized into 10 tab-separated columns as follows: 

	 - ID: The unique mGeNTE ID.
	 - Europarl_ID: The original sentence ID from Europarl's common-test-set 2.
	 - SET: Indicates whether the entry belongs to the Set-G or the Set-N subportion of the corpus.
	 - SRC: The English source sentence.
	 - REF-G: The gendered reference translation in the target language.
	 - REF-N: The gender-neutral reference in the target language, produced by a professional translator. 
	 - COMMON: Indicates whether the entry is part of mGeNTE common-set (yes/no).
	 - GENDER: For entries belonging to the Set-G, indicates if the entry is Feminine or Masculine (F/M).
     - REF-G_ann: Tokenized version of the gendered reference translation with target gendered words annotated.
     - G-WORDS: List of annotated target gendered words separated by "&". 

For entries of the common set, REF-N provides the gender-neutral reference translation n. 2. 

Each tsv file in **`mGeNTE_common`** comprises 200 entries organized into 11 tab-separated columns as follows:

	 - ID: The unique mGeNTE ID.
	 - Europarl_ID: The original sentence ID from Europarl's common-test-set 2.
	 - SET: Indicates whether the entry belongs to the Set-G or the Set-N subportion of the corpus.
	 - SRC: The English source sentence.
	 - REF-G: The gendered reference translation in the target language.
	 - REF-N1: The gender-neutral reference in the target language produced by Translator 1.
	 - REF-N2: The gender-neutral reference in the target language produced by Translator 2.
	 - REF-N3: The gender-neutral reference in the target language produced by Translator 3.
	 - GENDER: For entries belonging to the Set-G, indicates if the entry is Feminine or Masculine (F/M).
     - REF-G_ann: Tokenized version of the gendered reference translation with target gendered words annotated.
     - G-WORDS: List of annotated target gendered words separated by "&". 


## Dataset Creation

Refer to the [paper](https://arxiv.org/abs/2501.09409) for full details on dataset creation. 

### Curation Rationale

mGeNTE is designed to test gender-neutral language modeling and evaluate models’ ability to perform gender-neutral translations under desirable circumstances. 
In fact, when referents’ gender is unknown or irrelevant, undue gender inferences should not be made, and translation should be neutral. 
Instead, when a referent’s gender is relevant and known, MT should not over-generalize to neutral translations. 
The corpus hence consists of parallel sentences with mentions to human referents that equally represent two translation scenarios: 

- `Set-N`: featuring gender-ambiguous source sentences that require to be neutrally rendered in translation;
- `Set-G`: featuring gender-unambiguous source sentences, which shall
be properly rendered with gendered (masculine or feminine) forms in translation.

Across the three available language pairs, mGente features 987 fully parallel en-it/es/de/el segments to maximize comparability, i.e. `Parallel set`. 
Parallel multilingual instances feature the same string in the `SRC` data field. 

### Source Data

The dataset contains text data extracted and edited from the Europarl Corpus ([common test set 2](https://www.statmt.org/europarl/archives.html)), and all rights of the data belong to the European Union and/or respective copyright holders. 
Please refer to Europarl “[Terms of Use](https://www.statmt.org/europarl/archives.html)” for details.

### Annotations
For each sentence pair extracted from Europarl (src, ref), mGeNTE includes an additional reference in the target language, which differs from the original one only in that it refers to
the human entities with neutral expressions. 

The neutral reference translations were created by professionals based on the following language-specific guidelines: 
- [en-it](https://fbk.sharepoint.com/:b:/s/MTUnit/ET75jsZb-ZdJgfFcEsJiKo4Bugbw7E7gUutUyGlSz3U3mw?e=uNBOWc)
- [en-es](https://fbk.sharepoint.com/:b:/s/MTUnit/EXiEnDUA4QpJqALJF9R0U4oBy6j45uL8y2a04fSyRoeOlQ?e=EMiZsP)
- [en-de](https://fbk.sharepoint.com/:b:/s/MTUnit/ERpaw9A_2ENBnG0Y257xgdsB5l4Ntp2DdEcJGLHtVoEjcA?e=KZcjjw)
- [en-el](https://fbk.sharepoint.com/:b:/s/MTUnit/Eb2ibn4hKtRClImVT-pZKzgBOrnXjtz2SRrsKDz5W6K6Kw?e=g2ZuIL)

### Dataset Curators

The authors of mGeNTE are the dataset curators: en-it (A. Piergentili and B. Savoldi), en-es (Eleonora Cupini and B. Savoldi), en-de (M. Thin and A. Lauscher), en-el (E. Gkovedarou).
For curating efforts coordination, refer to Beatrice Savoldi (FBK) at <bsavoldi@fbk.eu>


### Licensing Information

The mGeNTE corpus is released under a Creative Commons Attribution 4.0 International license (CC BY 4.0).


## Citation
```bibtex
@inproceedings{savoldi2025mind,
  title={Mind the Inclusivity Gap: Multilingual Gender-Neutral Translation Evaluation with mGeNTE},
  author={
    Savoldi, Beatrice and 
    Attanasio, Giuseppe and 
    Cupin, Eleonora and 
    Gkovedarou, Eleni and 
    Hackenbuchner, Jani{\c{c}}a and 
    Lauscher, Anne and 
    Negri, Matteo and 
    Piergentili, Andrea and 
    Thind, Manjinder and 
    Bentivogli, Luisa
  },
  booktitle={Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing},
  year={2025},
  url={https://arxiv.org/abs/2501.09409}
}

```


## Contributions
Thanks to [@BSavoldi](https://huggingface.co/BSavoldi) for adding this dataset.