Hate Speech detection in Spanish

robertuito-hate-speech

Repository: https://github.com/pysentimiento/pysentimiento/

Model trained with SemEval 2019 Task 5: HatEval (SubTask B) corpus for Hate Speech detection in Spanish. Base model is RoBERTuito, a RoBERTa model trained in Spanish tweets.

It is a multi-classifier model, with the following classes:

HS: is it hate speech?
TR: is it targeted to a specific individual?
AG: is it aggressive?

Results

Results for the four tasks evaluated in pysentimiento. Results are expressed as Macro F1 scores

model	emotion	hate_speech	irony	sentiment
robertuito	0.560 ± 0.010	0.759 ± 0.007	0.739 ± 0.005	0.705 ± 0.003
roberta	0.527 ± 0.015	0.741 ± 0.012	0.721 ± 0.008	0.670 ± 0.006
bertin	0.524 ± 0.007	0.738 ± 0.007	0.713 ± 0.012	0.666 ± 0.005
beto_uncased	0.532 ± 0.012	0.727 ± 0.016	0.701 ± 0.007	0.651 ± 0.006
beto_cased	0.516 ± 0.012	0.724 ± 0.012	0.705 ± 0.009	0.662 ± 0.005
mbert_uncased	0.493 ± 0.010	0.718 ± 0.011	0.681 ± 0.010	0.617 ± 0.003
biGRU	0.264 ± 0.007	0.592 ± 0.018	0.631 ± 0.011	0.585 ± 0.011

Note that for Hate Speech, these are the results for Semeval 2019, Task 5 Subtask B (HS+TR+AG detection)

Citation

If you use this model in your research, please cite pysentimiento, RoBERTuito and HatEval papers:

% 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}
}