README.md

---
license: cc-by-4.0
task_categories:
- translation
- text-generation
language:
- en
- it
tags:
- gender
- inclusivity
- ethics
- fairness
- mt
- neomorphemes
multilinguality:
- multilingual
- translation
pretty_name: Neo-GATE
size_categories:
- n<1K
---
# Dataset card for Neo-GATE

**Homepage:** [https://mt.fbk.eu/neo-gate/](https://mt.fbk.eu/neo-gate/)

## Dataset summary

Neo-GATE is a bilingual corpus designed to benchmark the ability of machine translation (MT) systems to translate from English into Italian using gender-inclusive neomorphemes.
It is built upon GATE [(Rarrick et al., 2023)](https://dl.acm.org/doi/10.1145/3600211.3604675), a benchmark for the evaluation of gender rewriters and gender bias in MT.

Neo-GATE includes 841 `test` entries and 100 `dev` entries.
Each entry is composed of an English source sentence, three Italian references which only differ in the gendered terms, and an annotation that identifies the words of interest for gender-inclusive MT evaluation.

The source sentences are gender-ambiguous, i.e. they provide no information about the gender of human referents.
In our gender-inclusive MT task, words referring to human entities in the target language should express gender with neomorphemes, special characters or symbols that replace masculine and feminine inflectional morphemes.

Neo-GATE allows for the evaluation of any neomorpheme paradigm in Italian.
For more details see the [Adaptation](#adaptation) section below.


## Data Fields

`Neo-GATE.tsv` includes the following columns:

- **#:** The number of the entry within Neo-GATE.
- **GATE-ID:** The ID of the original entry in GATE, composed of a prefix indicating the subset of origin within GATE (e.g., `IT_2_variants`) followed by a serial number indicating the position of the entry within that subset (i.e., `001`, `002`, etc.).
- **SPLIT:** Either `dev` or `test`, indicating whether the entry belongs to the dev set or the test set.
- **SOURCE:** The English source sentence.
- **REF-M:** The Italian reference where all gender-marked terms are masculine. 
- **REF-F:** The Italian reference where all gender-marked terms are feminine. 
- **REF-TAGGED:** The Italian reference where all gender-marked terms are tagged with Neo-GATE's annotation.
- **ANNOTATION:** The annotation for that entry.

## Dataset creation

Please refer to [the original paper](https://arxiv.org/search/?searchtype=author&query=Piergentili%2C+A) for full details on dataset creation.

## Adaptation
To adapt Neo-GATE to the desired neomorpheme paradigm, a `.json` file mapping Neo-GATE's tagset to the desired forms is required.
See `schwa.json` for an example.
For more information on the tagset, see Table 8 in [the original paper](https://arxiv.org/search/?searchtype=author&query=Piergentili%2C+A). 

To create the adapted references and annotations, use the `neo-gate_format.py` script with the following syntax:

    python neo-gate_adapt.py --tagset JSON_FILE_PATH --out OUTPUT_FILE_NAME

This command will create two files: `OUTPUT_FILE_NAME.ref`, containing the adapted references, and `OUTPUT_FILE_NAME.ann`, containing the adapted annotations.

For instance, to generate the references and the annotations adapted to the schwa paradigm provided in the example file `schwa.json`, the following command can be used:

    python neo-gate_adapt.py --tagset schwa.json --out neogate_schwa

This will create the two files `neogate_schwa.ref` and `neogate_schwa.ann`.

If the `Neo-GATE.tsv` file is located in a different directory, the path to it can be passed to the script with the optional argument `--neogate`.

## Evaluation

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

## Licensing Information

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

## Citation

If you use Neo-GATE in your work, please consider citing the following paper: