注意多读讽刺检测
Attentional Multi-Reading Sarcasm Detection
摘要：
识别讽刺通常需要深入了解多种信息来源，包括话语，对话上下文和现实世界的事实。目前的大多数讽刺检测系统只考虑孤立的话语。有一些有限的尝试考虑到会话上下文。在本文中，我们提出了一个可解释的端到端模型，结合信息的话语和会话背景下检测讽刺，并通过实证评估证明其有效性。我们还研究了所提出的模型的行为，为模型的决策提供解释。重要的是，我们的模型能够确定话语和会话背景对模型决策的影响。最后，我们提供了一个消融研究来说明所提出的模型的不同组件的影响。
英文摘要：
Recognizing sarcasm often requires a deep understanding of multiple sources of information, including the utterance, the conversational context, and real world facts. Most of the current sarcasm detection systems consider only the utterance in isolation. There are some limited attempts toward taking into account the conversational context. In this paper, we propose an interpretable end-to-end model that combines information from both the utterance and the conversational context to detect sarcasm, and demonstrate its effectiveness through empirical evaluations. We also study the behavior of the proposed model to provide explanations for the model's decisions. Importantly, our model is capable of determining the impact of utterance and conversational context on the model's decisions. Finally, we provide an ablation study to illustrate the impact of different components of the proposed model.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
