GeNTE Evaluator

The Gender-Neutral Translation (GeNTE) Evaluator is a sequence classification model used for evaluating inclusive rewriting and translations into Italian with the GeNTE corpus. It is built by fine-tuning the RoBERTa-based UmBERTo model.

More details on the training process and the reproducibility can be found in the official repository and the paper.

Usage

You can use the GeNTE Evaluator as follows:

from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch

# load the tokenizer of UmBERTo
tokenizer = AutoTokenizer.from_pretrained("Musixmatch/umberto-commoncrawl-cased-v1", do_lower_case=False)

# load GeNTE Evaluator
model = AutoModelForSequenceClassification.from_pretrained("FBK-MT/GeNTE-evaluator")

# neutral example
sample = ("Condividiamo il parere di chi ha presentato la relazione che  ha posto "
          "notevole enfasi sull'informazione in relazione ai rischi e sulla trasparenza, "
          "in particolare nel campo sanitario e della sicurezza.")
input = tokenizer(sample, return_tensors='pt', truncation=True, max_length=64)

with torch.no_grad():
  probs = model(**input).logits

predicted_label = torch.argmax(probs, dim=1).item()
print(model.config.id2label[predicted_label])

Citation

@inproceedings{savoldi-etal-2024-prompt,
    title = "A Prompt Response to the Demand for Automatic Gender-Neutral Translation",
    author = "Savoldi, Beatrice  and
      Piergentili, Andrea  and
      Fucci, Dennis  and
      Negri, Matteo  and
      Bentivogli, Luisa",
    editor = "Graham, Yvette  and
      Purver, Matthew",
    booktitle = "Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = mar,
    year = "2024",
    address = "St. Julian{'}s, Malta",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.eacl-short.23",
    pages = "256--267",
    abstract = "Gender-neutral translation (GNT) that avoids biased and undue binary assumptions is a pivotal challenge for the creation of more inclusive translation technologies. Advancements for this task in Machine Translation (MT), however, are hindered by the lack of dedicated parallel data, which are necessary to adapt MT systems to satisfy neutral constraints. For such a scenario, large language models offer hitherto unforeseen possibilities, as they come with the distinct advantage of being versatile in various (sub)tasks when provided with explicit instructions. In this paper, we explore this potential to automate GNT by comparing MT with the popular GPT-4 model. Through extensive manual analyses, our study empirically reveals the inherent limitations of current MT systems in generating GNTs and provides valuable insights into the potential and challenges associated with prompting for neutrality.",
}

Contributions

Thanks to @dfucci for adding this model.