metadata

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/

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), 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 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 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.

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.

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: