在西班牙语中检测仇恨言论

robertuito仇恨言论

仓库： https://github.com/pysentimiento/pysentimiento/

使用SemEval 2019任务5: HatEval（子任务B）语料库训练的模型，用于检测西班牙语中的仇恨言论。基础模型是 RoBERTuito ，是一个在西班牙推文中训练的RoBERTa模型。

这是一个多分类器模型，包括以下类别：

• HS：是否是仇恨言论？
• TR：是否针对特定个体？
• AG：是否具有攻击性？

结果

pysentimiento中评估的四个任务的结果。结果以宏F1得分表示。

请注意，对于仇恨言论，这些是Semeval 2019任务5子任务B（HS+TR+AG检测）的结果

引用

如果您在研究中使用此模型，请引用pysentimiento、RoBERTuito和HatEval的论文：

% Pysentimiento
@misc{perez2021pysentimiento,
      title={pysentimiento: A Python Toolkit for Sentiment Analysis and SocialNLP tasks},
      author={Juan Manuel Pérez and Juan Carlos Giudici and Franco Luque},
      year={2021},
      eprint={2106.09462},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
% RoBERTuito paper
@inproceedings{perez-etal-2022-robertuito,
    title = "{R}o{BERT}uito: a pre-trained language model for social media text in {S}panish",
    author = "P{\'e}rez, Juan Manuel  and
      Furman, Dami{\'a}n Ariel  and
      Alonso Alemany, Laura  and
      Luque, Franco M.",
    booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
    month = jun,
    year = "2022",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://aclanthology.org/2022.lrec-1.785",
    pages = "7235--7243",
    abstract = "Since BERT appeared, Transformer language models and transfer learning have become state-of-the-art for natural language processing tasks. Recently, some works geared towards pre-training specially-crafted models for particular domains, such as scientific papers, medical documents, user-generated texts, among others. These domain-specific models have been shown to improve performance significantly in most tasks; however, for languages other than English, such models are not widely available. In this work, we present RoBERTuito, a pre-trained language model for user-generated text in Spanish, trained on over 500 million tweets. Experiments on a benchmark of tasks involving user-generated text showed that RoBERTuito outperformed other pre-trained language models in Spanish. In addition to this, our model has some cross-lingual abilities, achieving top results for English-Spanish tasks of the Linguistic Code-Switching Evaluation benchmark (LinCE) and also competitive performance against monolingual models in English Twitter tasks. To facilitate further research, we make RoBERTuito publicly available at the HuggingFace model hub together with the dataset used to pre-train it.",
}
% HatEval paper

@inproceedings{hateval2019semeval,
  title={SemEval-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter},
  author={Basile, Valerio and Bosco, Cristina and Fersini, Elisabetta and Nozza, Debora and Patti, Viviana and Rangel, Francisco and Rosso, Paolo and Sanguinetti, Manuela},
  booktitle={Proceedings of the 13th International Workshop on Semantic Evaluation (SemEval-2019)},
  year={2019},
  publisher= {Association for Computational Linguistics}
}