A Fusion Pretrained Approach for Identifying the Cause of Sarcasm Remarks

Qiudan Li (), David Jingjun Xu (), Haoda Qian (), Linzi Wang (), Minjie Yuan () and Daniel Dajun Zeng ()
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Qiudan Li: State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
David Jingjun Xu: Department of Information Systems, College of Business, City University of Hong Kong, Hong Kong, China
Haoda Qian: State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; and School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
Linzi Wang: State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; and School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
Minjie Yuan: State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; and School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
Daniel Dajun Zeng: State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; and School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China

INFORMS Journal on Computing, 2025, vol. 37, issue 2, 465-479

Abstract: Sarcastic remarks often appear in social media and e-commerce platforms to express almost exclusively negative emotions and opinions on certain instances, such as dissatisfaction with a purchased product or service. Thus, the detection of sarcasm allows merchants to timely resolve users’ complaints. However, detecting sarcastic remarks is difficult because of its common form of using counterfactual statements. The few studies that are dedicated to detecting sarcasm largely ignore what sparks these sarcastic remarks, which could be because of an empty promise of a merchant’s product description. This study formulates a novel problem of sarcasm cause detection that leverages domain information, dialogue context information, and sarcasm sentences by proposing a pretrained language model-based approach equipped with a novel hybrid multihead fusion-attention mechanism that combines self-attention, target-attention, and a feed-forward neural network. The domain information and the dialogue context information are then interactively fused to obtain the domain-specific dialogue context representation, and bidirectionally enhanced sarcasm-cause pair representations are generated for detecting sarcasm spark. Experimental results on real-world data sets demonstrate the efficacy of the proposed model. The findings of this study contribute to the literature on sarcasm cause detection and provide business value to relevant stakeholders and consumers.

Keywords: sarcasm cause detection; pretrained language model; fusion-attention mechanism; dynamic interactive semantics; sarcasm spark (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:inm:orijoc:v:37:y:2025:i:2:p:465-479

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