A Fusion Pretrained Approach for Identifying the Cause of Sarcasm Remarks

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. History: Accepted by Ram Ramesh, Area Editor for Data Science and Machine Learning. Funding: This work was partially supported by the National Natural Science Foundation of China [Grants 72293575, 62071467, and 62141608] and the Research Grant Council of the Hong Kong Special Administrative Region, China [Grants 11500322 and 11500421]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0285 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0285 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .