Dataset · Hugging Face

FBK-MT/Neo-GATE

Name: Neo-GATE
Creator: FBK-MT
License: cc-by-4.0
Keywords: task_categories:translation, task_categories:text-generation, multilinguality:multilingual, multilinguality:translation, language:en, language:it, license:cc-by-4.0, size_categories:1K<n<10K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us

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 (Neo-GATE.tsv) and 100 dev entries (Neo-GATE-dev.tsv).… See the full description on the dataset page: https://huggingface.co/datasets/FBK-MT/Neo-GATE.

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Author / organization	FBK-MT
License (as declared)	cc-by-4.0
Downloads	192 (as reported by Hugging Face)
Likes	11
Created	30 April 2024
Last updated at source	28 October 2024

genderinclusivityethicsfairnessmtneomorphemes

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16 Oct 2026 Description: 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-inclusi → 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-inclusi

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Silytics shows public metadata from Hugging Face and links to the original. Information may be incomplete or out of date. Last refreshed 18 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.