Sarcasm indicates the literal meaning is contrary to the real attitude. Considering the
popularity and complementarity of image-text data, we investigate the task of multi-modal …

Multimodal sarcasm detection is an important research topic in natural language processing
and multimedia computing, and benefits a wide range of applications in multiple domains …

Multi-modal sarcasm detection (MSD), which aims to identify whether a given sample with
multimodal information (ie, text and image) is sarcastic, has garnered widespread attention …

Multimodal misinformation on online social platforms is becoming a critical concern due to
increasing credibility and easier dissemination brought by multimedia content, compared to …

Multi-modal sarcasm detection has attracted much recent attention. Nevertheless, the
existing benchmark (MMSD) has some shortcomings that hinder the development of reliable …

Sarcasm is a form of sentiment expression that highlights the disparity between a person's
true intentions and the content they explicitly present. With the exponential increase in …

Social media abounds with multimodal sarcasm, and identifying sarcasm targets is
particularly challenging due to the implicit incongruity not directly evident in the text and …

Multimodal sarcasm detection aims to identify whether the literal expression is contrary to
the authentic attitude within multimodal data. Sarcasm incongruity method has been …

Abstract Joint Multi-modal Aspect-based Sentiment Analysis (JMASA) is a challenging task
that seeks to identify all aspect-sentiment pairs from multimodal data. Current JMASA …

Sarcasm embodies a linguistic phenomenon that highlights a significant incongruity
between the literal meanings of words and intended attitudes. With the proliferation of image …