1 citations · 1 across the 8 of their papers we have counts for
6 papers · 1 filter
MuVaC: A Variational Causal Framework for Multimodal Sarcasm Understanding in Dialogues
Diandian Guo, Fangfang Yuan, Cong Cao +5
The prevalence of sarcasm in multimodal dialogues on the social platforms presents a crucial yet challenging task for understanding the true intent behind online content. Comprehen…
Emotion Transfer with Enhanced Prototype for Unseen Emotion Recognition in Conversation
Kun Peng, Cong Cao, Hao Peng +5
Current Emotion Recognition in Conversation (ERC) research follows a closed-domain assumption. However, there is no clear consensus on emotion classification in psychology, which p…
Dialogues Aspect-based Sentiment Quadruple Extraction via Structural Entropy Minimization Partitioning
Kun Peng, Cong Cao, Hao Peng +5
Dialogues Aspect-based Sentiment Quadruple Extraction (DiaASQ) aims to extract all target-aspect-opinion-sentiment quadruples from a given multi-round, multi-participant dialogue.…
T-T: Table Transformer for Tagging-based Aspect Sentiment Triplet Extraction
Kun Peng, Chaodong Tong, Cong Cao +6
Aspect sentiment triplet extraction (ASTE) aims to extract triplets composed of aspect terms, opinion terms, and sentiment polarities from given sentences. The table tagging method…
Multi-View Incongruity Learning for Multimodal Sarcasm Detection
Diandian Guo, Cong Cao, Fangfang Yuan +5
Multimodal sarcasm detection (MSD) is essential for various downstream tasks. Existing MSD methods tend to rely on spurious correlations. These methods often mistakenly prioritize…
Can Multimodal Large Language Model Think Analogically?
Diandian Guo, Cong Cao, Fangfang Yuan +4
Analogical reasoning, particularly in multimodal contexts, is the foundation of human perception and creativity. Multimodal Large Language Model (MLLM) has recently sparked conside…