Summary of Vyang-net: a Novel Multi-modal Sarcasm Recognition Model by Uncovering Visual, Acoustic and Glossary Features, By Ananya Pandey et al.
VyAnG-Net: A Novel Multi-Modal Sarcasm Recognition Model by Uncovering Visual, Acoustic and Glossary Features
by Ananya Pandey, Dinesh Kumar Vishwakarma
First submitted to arxiv on: 5 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Audio and Speech Processing (eess.AS)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel multi-modal approach for recognizing sarcasm in conversation, which combines computer vision and natural language processing techniques. The model incorporates a lightweight depth attention module to focus on crucial visual features and an attentional tokenizer strategy to extract context-specific textual information. The approach is tested on the MUSTaRD video dataset, achieving 79.86% accuracy for speaker-dependent and 76.94% for speaker-independent configurations. This outperforms existing methods. The paper also conducts a cross-dataset analysis using MUStARD++ to evaluate the model’s adaptability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about recognizing sarcasm in conversations, like when someone says something but really means the opposite. Right now, most research focuses on text only, but this paper suggests we should also consider audio, facial expressions, and body language to make sure our sarcasm recognition is accurate. The authors propose a new approach that uses computer vision and natural language processing together to identify sarcastic comments. They test their method using a video dataset and get impressive results, beating existing methods. This shows that their approach can work well even when it’s applied to new data. |
Keywords
» Artificial intelligence » Attention » Multi modal » Natural language processing » Tokenizer
