README.md

---
configs:
  - config_name: main
    data_files:
      - split: test
        path: "GeNTE.tsv"
    default: true
  - config_name: common
    data_files:
      - split: test
        path: "GeNTE_common.tsv"



annotations_creators:
- expert-generated
language:
- en
- it
language_creators:
- expert-generated
license:
- cc-by-4.0
multilinguality:
- multilingual
- translation
paperswithcode_id: null
pretty_name: 'GeNTE: Gender-Neutral Translation Evaluation'
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 GeNTE

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


### Dataset Summary

GeNTE (**Ge**nder-**N**eutral **T**ranslation **E**valuation) is a natural, bilingual corpus designed to benchmark the ability of machine translation systems to generate gender-neutral translations.

Built from European Parliament speeches, GeNTE comprises a subset of the English-Italian portion of the [Europarl corpus](https://www.statmt.org/europarl/archives.html). 
GeNTE comprises 1500 parallel sentences, 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

**Machine Translation**

GeNTE supports cross-lingual (en-it) and intra-lingual (it-it) gender inclusive translation tasks.

Refer to the paper [*Hi Guys* or *Hi Folks?* Benchmarking Gender-Neutral Machine Translation with the GeNTE Corpus](https://aclanthology.org/2023.emnlp-main.873/) for additional details on evaluation with GeNTE.

The evaluation code is available at [fbk-NEUTR-evAL](https://github.com/hlt-mt/fbk-NEUTR-evAL/blob/main/solutions/GeNTE.md).


## Dataset Structure

### Data Instances

The dataset consists of two configuration types (`main` and `common`) corrisponding to the files: 

- **`GeNTE.tsv`:** The complete GeNTE corpus and its set annotations
-  **`GeNTE_common.tsv`:** Subset of the GeNTE corpus that comprises 3 alternative gender-neutral reference translations


### Data Fields

**`GeNTE.tsv`** is organized into 8 tab-separated columns as follows: 

	 - ID: The unique GeNTE 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 Italian reference translation.
	 - REF-N: The gender-neutral Italian reference, produced by a professional translator. 
	 - COMMON: Indicates whether the entry is part of GeNTE common-set (yes/no).
	 - GENDER: For entries belonging to the Set-G, indicates if the the entry is Feminine or Masculine (F/M).

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

**`GeNTE-common.tsv`** comprises 200 entries organized into 9 tab-separated columns as follows:

	 - ID: The unique GeNTE 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 Italian reference translation.
	 - REF-N1: The gender-neutral Italian reference produced by Translator 1.
	 - REF-N2: The gender-neutral Italian reference produced by Translator 2.
	 - REF-N3: The gender-neutral Italian reference produced by Translator 3.
	 - GENDER: For entries belonging to the Set-G, indicates if the the entry is Feminine or Masculine (F/M).


## Dataset Creation

Refer to the original [paper](https://aclanthology.org/2023.emnlp-main.873/) for full details on dataset creation. 

### Curation Rationale

GeNTE is designed to 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 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.

### 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, it-ref),GeNTE includes an additional Italian reference, which differs from the original one only in that it refers to
the human entities with neutral expressions. 

The neutral reference translation were created by professionals based on the following [guidelines](https://drive.google.com/file/d/1TvV6NQoXiPHNSUHYlf4NFhef1_PKncF6/view?usp=sharing).

### Dataset Curators

The authors of GeNTE are the dataset curators.

- Beatrice Savoldi (FBK): bsavoldi@fbk.eu
- Luisa Bentivogli (FBK): bentivo@fbk.eu
- Andrea Piergentili (FBK): apiergentili@fbk.eu


### Licensing Information

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



## Citation
```bibtex
@inproceedings{piergentili-etal-2023-hi,
    title = "Hi Guys or Hi Folks? Benchmarking Gender-Neutral Machine Translation with the {G}e{NTE} Corpus",
    author = "Piergentili, Andrea and 
      Savoldi, Beatrice  and
      Fucci, Dennis  and
      Negri, Matteo  and
      Bentivogli, Luisa",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.emnlp-main.873",
    doi = "10.18653/v1/2023.emnlp-main.873",
    pages = "14124--14140"
}
```


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