@inproceedings{bangerter-etal-2023-unisa,
    title = "Unisa at {S}em{E}val-2023 Task 3: A {SHAP}-based method for Propaganda Detection",
    author = "Bangerter, Micaela  and
      Fenza, Giuseppe  and
      Gallo, Mariacristina  and
      Loia, Vincenzo  and
      Volpe, Alberto  and
      Maio, Carmen De  and
      Stanzione, Claudio",
    editor = {Ojha, Atul Kr.  and
      Do{\u{g}}ru{\"o}z, A. Seza  and
      Da San Martino, Giovanni  and
      Tayyar Madabushi, Harish  and
      Kumar, Ritesh  and
      Sartori, Elisa},
    booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.semeval-1.122",
    doi = "10.18653/v1/2023.semeval-1.122",
    pages = "885--891",
    abstract = "This paper presents proposed solutions for addressing two subtasks in SemEval-2023 Task 3: {``}Detecting the Genre, the Framing, and the Persuasion techniques in online news in a multi-lingual setup. In subtask 1, {``}News Genre Categorisation, the goal is to classify a news article as an opinion, a report, or a satire. In subtask 3, {``}Detection of Persuasion Technique, the system must reveal persuasion techniques used in each news article paragraph choosing among23 defined methods. Solutions leverage the application of the eXplainable Artificial Intelligence (XAI) method, Shapley Additive Explanations (SHAP). In subtask 1, SHAP was used to understand what was driving the model to fail so that it could be improved accordingly. In contrast, in subtask 3, a re-calibration of the Attention Mechanism was realized by extracting critical tokens for each persuasion technique. The underlying idea is the exploitation of XAI for countering the overfitting of the resulting model and attempting to improve the performance when there are few samples in the training data. The achieved performance on English for subtask 1 ranked 6th with an F1-score of 58.6{\%} (despite 78.4{\%} of the 1st) and for subtask 3 ranked 12th with a micro-averaged F1-score of 29.8{\%} (despite 37.6{\%} of the 1st).",
}

This paper presents proposed solutions for addressing two subtasks in SemEval-2023 Task 3: {``}Detecting the Genre, the Framing, and the Persuasion techniques in online news in a multi-lingual setup. In subtask 1, {``}News Genre Categorisation, the goal is to classify a news article as an opinion, a report, or a satire. In subtask 3, {``}Detection of Persuasion Technique, the system must reveal persuasion techniques used in each news article paragraph choosing among23 defined methods. Solutions leverage the application of the eXplainable Artificial Intelligence (XAI) method, Shapley Additive Explanations (SHAP). In subtask 1, SHAP was used to understand what was driving the model to fail so that it could be improved accordingly. In contrast, in subtask 3, a re-calibration of the Attention Mechanism was realized by extracting critical tokens for each persuasion technique. The underlying idea is the exploitation of XAI for countering the overfitting of the resulting model and attempting to improve the performance when there are few samples in the training data. The achieved performance on English for subtask 1 ranked 6th with an F1-score of 58.6{\%} (despite 78.4{\%} of the 1st) and for subtask 3 ranked 12th with a micro-averaged F1-score of 29.8{\%} (despite 37.6{\%} of the 1st).
Bangerter, Micaela Fenza, Giuseppe Gallo, Mariacristina Loia, Vincenzo Volpe, Alberto Maio, Carmen De Stanzione, Claudio
Unisa at SemEval-2023 Task 3: A SHAP-based method for Propaganda Detection