Results 281 to 290 of about 4,714,839 (322)

EilMoB: Emotion-aware Incongruity Learning and Modality Bridging Network for Multi-modal Sarcasm Detection

International Conference on Multimedia Retrieval
Multi-modal sarcasm detection aims to identify sarcasm by fusing textual and visual information, with incongruity serving as a central cue. Although existing incongruity-based approaches have achieved notable success, they often fail to fully leverage ...
Haochen Zhao, Yongxiu Xu, Xinkui Lin, Jiarui Lu, Hongbo Xu, Yubin Wang   +5 more
semanticscholar   +1 more source

Beyond Spurious Cues: Adaptive Multi-Modal Fusion via Mixture-of-Experts for Robust Sarcasm Detection

Mathematics
Sarcasm is a complex emotional expression often marked by semantic contrast and incongruity between textual and visual modalities. In recent years, multi-modal sarcasm detection (MMSD) has emerged as a vital task in affective computing. However, existing
Guilong Zhao, Yixia Zhao, Xiangrong Yin, L. Lin, Jizhao Zhu   +4 more
semanticscholar   +1 more source

Intermediate-Task Transfer Learning: Leveraging Sarcasm Detection for Stance Detection

arXiv.org
Stance Detection (SD) on social media has emerged as a prominent area of interest with implications for social business and political applications thereby garnering escalating research attention within NLP.
Gibson Nkhata, Susan Gauch
semanticscholar   +1 more source

Leveraging Large Language Models for Sarcastic Speech Annotation in Sarcasm Detection

Interspeech
Sarcasm fundamentally alters meaning through tone and context, yet detecting it in speech remains a challenge due to data scarcity. In addition, existing detection systems often rely on multimodal data, limiting their applicability in contexts where only
Zhu Li, Yuqing Zhang, Xiyuan Gao, Shekhar Nayak, Matt Coler   +4 more
semanticscholar   +1 more source

Multimodal Arabic Sarcasm Detection Using CNN and BiLSTM

2025 International Conference on Intelligent Systems: Theories and Applications (SITA)
Sarcasm detection in Arabic social media is difficult due to linguistic diversity and the need for context beyond text. Most existing work is limited to single-modality approaches.
Ayoub Ben Cheikhi, E. Nfaoui
semanticscholar   +1 more source

Commander-GPT: Fully Unleashing the Sarcasm Detection Capability of Multi-Modal Large Language Models

arXiv.org
Sarcasm detection, as a crucial research direction in the field of Natural Language Processing (NLP), has attracted widespread attention. Traditional sarcasm detection tasks have typically focused on single-modal approaches (e.g., text), but due to the ...
Yazhou Zhang, Chunwang Zou, Bo Wang, Jing Qin   +3 more
semanticscholar   +1 more source

Think Twice Before You Judge: Mixture of Dual Reasoning Experts for Multimodal Sarcasm Detection

arXiv.org
Multimodal sarcasm detection has attracted growing interest due to the rise of multimedia posts on social media. Understanding sarcastic image-text posts often requires external contextual knowledge, such as cultural references or commonsense reasoning ...
Soumyadeep Jana, Abhrajyoti Kundu, Sanasam Ranbir Singh   +2 more
semanticscholar   +1 more source

Ensemble Learning-Based Sarcasm Detection in Hinglish Tweets Using Word2Vec Embedding

2025 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI)
Sarcasm detection in social media content has gained significant attention due to its implications in natural language processing (NLP) tasks, such as sentiment analysis and conversational AI.
A. Acharya, R. Goyal
semanticscholar   +1 more source

Automated Sarcasm Detection in English Tweets Using CCNN and ELLSTM with Text and Emoji Embeddings

2025 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE)
Automatic detection of sarcasm is one of the most challenging tasks in natural language processing; hence, extending it into the realm of English tweets-where sarcasm is usually conveyed by text and emojis-would add to the complexity. The paper addresses
Shaikh Ambreen Mohd Ibrahim, Manoj M. Deshpande, V. N. Pawar   +2 more
semanticscholar   +1 more source

Evaluating Open-Source Vision-Language Models for Multimodal Sarcasm Detection

2025 IEEE International Conference on Data Mining Workshops (ICDMW)
Recent advances in open-source vision-language models (VLMs) offer new opportunities for understanding complex and subjective multimodal phenomena such as sarcasm. In this work, we evaluate seven state-of-the-art VLMs - BLIP2, InstructBLIP, OpenFlamingo,
Saroj Basnet, Shafkat Farabi, Tharindu Ranasinghe, Diptesh Kanojia, Marcos Zampieri   +4 more
semanticscholar   +1 more source