{
  "dataset_name" : "GeNTE",
  "description": "GeNTE (Gender-Neutral Translation Evaluation) is a natural, bilingual corpus designed to benchmark the ability of machine translation systems to generate gender-neutral translations.",
  "citation": "@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\"}",
  "homepage": " https://mt.fbk.eu/gente/",
  "license": "cc-by-4.0",
  "task_ids": ["translation", "text-generation"],
  "splits": {
    "test": {
      "num_examples": 1500
    },
    "common": {
      "num_examples": 200
    }
  },
  "version": "1.0"
}