Sarcasm detection is important for several NLP tasks such as sentiment identification in product reviews, user feedback, and online forums. It is a challenging task requiring a deep understanding of language, context, and world knowledge. In this paper, we investigate whether incorporating commonsense knowledge helps in sarcasm detection. For this, we incorporate commonsense knowledge into the prediction process using a graph convolution network with pre-trained language model embeddings as input. Our experiments with three sarcasm detection datasets indicate that the approach does not outperform the baseline model. We perform an exhaustive set of experiments to analyze where commonsense support adds value and where it hurts classification. Our implementation is publicly available at: https://github.com/brcsomnath/commonsense-sarcasm.

中文翻译：

---

常识是否有助于发现讽刺？

讽刺检测对于多项 NLP 任务非常重要，例如产品评论中的情感识别、用户反馈和在线论坛。这是一项具有挑战性的任务，需要对语言、上下文和世界知识有深刻的理解。在本文中，我们调查了结合常识知识是否有助于讽刺检测。为此，我们使用具有预训练语言模型嵌入作为输入的图卷积网络将常识知识纳入预测过程。我们对三个讽刺检测数据集的实验表明，该方法没有优于基线模型。我们进行了一组详尽的实验，以分析常识支持在哪些方面增加了价值以及在哪些方面损害了分类。我们的实施可在以下网址公开获得：https://github.com/brcsomnath/commonsense-sarcasm。