Preprint
A Transformer and Prototype-based Interpretable Model for Contextual Sarcasm Detection
14 Mar 2025
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.
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Details
- Title
- A Transformer and Prototype-based Interpretable Model for Contextual Sarcasm Detection
- Creators
- Ximing WenRezvaneh Rezapour
- Resource Type
- Preprint
- Language
- English
- Academic Unit
- Information Science; College of Computing and Informatics
- Other Identifier
- 991022041190804721