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

TL;DR

BNS-Net is a dual-channel sarcasm detection model that considers behavior-level and sentence-level conflicts, incorporating external sentiment knowledge to improve accuracy and achieve state-of-the-art results.

Contribution

The paper introduces BNS-Net, a novel dual-channel model that effectively captures behavior and sentence conflicts for sarcasm detection, integrating external sentiment knowledge.

Findings

BNS-Net outperforms existing methods on three public datasets.

The model effectively captures implicit and explicit conflicts in sarcastic texts.

Ablation studies confirm the importance of both channels and external sentiment knowledge.

Abstract

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.