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Sarcasm detection is a binary classification task that aims to determinewhether a given utterance is sarcastic. Over the past decade, sarcasm detectionhas evolved from classical pattern recognition to deep learning approaches,where features such as user profile, punctuation and sentiment words have beencommonly employed for sarcasm detection. In real-life sarcastic expressions,behaviors without explicit sentimental cues often serve as carriers of implicitsentimental meanings. Motivated by this observation, we proposed a dual-channelsarcasm detection model named BNS-Net. The model considers behavior andsentence conflicts in two channels. Channel 1: Behavior-level Conflict Channelreconstructs the text based on core verbs while leveraging the modifiedattention mechanism to highlight conflict information. Channel 2:Sentence-level Conflict Channel introduces external sentiment knowledge tosegment the text into explicit and implicit sentences, capturing conflictsbetween them. To validate the effectiveness of BNS-Net, several comparative andablation experiments are conducted on three public sarcasm datasets. Theanalysis and evaluation of experimental results demonstrate that the BNS-Neteffectively identifies sarcasm in text and achieves the state-of-the-artperformance.

BNS-Net: A Dual-channel Sarcasm Detection Method Considering Behavior-level and Sentence-level Conflicts