  Sarcasm detection is a binary classification task that aims to determine
whether a given utterance is sarcastic. Over the past decade, sarcasm detection
has evolved from classical pattern recognition to deep learning approaches,
where features such as user profile, punctuation and sentiment words have been
commonly employed for sarcasm detection. In real-life sarcastic expressions,
behaviors without explicit sentimental cues often serve as carriers of implicit
sentimental meanings. Motivated by this observation, we proposed a dual-channel
sarcasm detection model named BNS-Net. The model considers behavior and
sentence conflicts in two channels. Channel 1: Behavior-level Conflict Channel
reconstructs the text based on core verbs while leveraging the modified
attention mechanism to highlight conflict information. Channel 2:
Sentence-level Conflict Channel introduces external sentiment knowledge to
segment the text into explicit and implicit sentences, capturing conflicts
between them. To validate the effectiveness of BNS-Net, several comparative and
ablation experiments are conducted on three public sarcasm datasets. The
analysis and evaluation of experimental results demonstrate that the BNS-Net
effectively identifies sarcasm in text and achieves the state-of-the-art
performance.
