用原型网络+情感嵌入，让模型自解释讽刺语义。
A Transformer and Prototype-based Interpretable Model for Contextual Sarcasm Detection
- 基于变换器与原型网络，结合情感嵌入捕捉上下文讽刺
- 在三个公开数据集上超越当前最优模型，准确率提升显著
- 通过相似样例生成解释，无需额外解释工具
讽刺检测因修辞特性，对情感分析系统构成独特挑战。传统系统在识别直接情绪表达时表现良好，但在处理讽刺中字面与真实情感的矛盾时效果不佳。由于变换器语言模型（LMs）擅长捕捉上下文语义，我们提出一种结合LM与原型网络的方法，通过情感嵌入实现可解释的讽刺检测。该方法天然可解释，无需额外后处理解释技术。我们在三个公开基准数据集上测试，结果表明模型性能优于当前最优水平。同时，原型层通过参考时间中的相似样本生成解释，增强模型内在可解释性。消融实验进一步验证了使用情感原型构建的不一致损失（incongruity loss）的有效性。
原文摘要 · Abstract (English)
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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