@inproceedings{wen-rezapour-2026-transformer,
    title = "A Transformer and Prototype-based Interpretable Model for Contextual Sarcasm Detection",
    author = "Wen, Ximing  and
      Rezapour, Rezvaneh",
    editor = "Barnes, Jeremy  and
      Barriere, Valentin  and
      De Clercq, Orph{\'e}e  and
      Klinger, Roman  and
      Nouri, C{\'e}lia  and
      Nozza, Debora  and
      Singh, Pranaydeep",
    booktitle = "The Proceedings for the 15th Workshop on Computational Approaches to Subjectivity, Sentiment Social Media Analysis ({WASSA} 2026)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.wassa-1.21/",
    pages = "278--288",
    ISBN = "979-8-89176-378-4",
    abstract = "Sarcasm detection, with its figurative nature, poses unique challenges for affective systems designed to perform sentiment analysis. While these systems typically perform well at identifying direct expressions of emotion, they struggle with sarcasm{'}s inherent contradiction between literal and intended sentiment. Since transformer-based language models (LMs) are known for their efficient ability to capture contextual meanings, we propose a method that leverages LMs and prototype-based networks, enhanced by sentiment embeddings to conduct interpretable sarcasm detection. Our approach is intrinsically interpretable without extra post-hoc interpretability techniques. We test our model on three public benchmark datasets and show that our model outperforms the current state-of-the-art. At the same time, the prototypical layer enhances the model{'}s inherent interpretability by generating explanations through similar examples in the reference time. Furthermore, we demonstrate the effectiveness of incongruity loss in the ablation study, which we construct using sentiment prototypes."
}