Abstract
Sarcasm is a pervasive phenomenon in today’s social media platforms such as Twitter and Reddit. These platforms allow users to create multi-modal messages, including texts, images, and videos. Existing multi-modal sarcasm detection methods either simply concatenate the features from multi modalities or fuse the multi modalities information in a designed manner. However, they ignore the incongruity character in sarcastic utterance, which is often manifested between modalities or within modalities. Inspired by this, we propose a BERT architecture-based model, which concentrates on both intra and inter-modality incongruity for multi-modal sarcasm detection. To be specific, we are inspired by the idea of self-attention mechanism and design inter-modality attention to capturing inter-modality incongruity. In addition, the co-attention mechanism is applied to model the contradiction within the text. The incongruity information is then used for prediction. The experimental results demonstrate that our model achieves state-of-the-art performance on a public multi-modal sarcasm detection dataset.- Anthology ID:
- 2020.findings-emnlp.124
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2020
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Trevor Cohn, Yulan He, Yang Liu
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1383–1392
- Language:
- URL:
- https://aclanthology.org/2020.findings-emnlp.124
- DOI:
- 10.18653/v1/2020.findings-emnlp.124
- Cite (ACL):
- Hongliang Pan, Zheng Lin, Peng Fu, Yatao Qi, and Weiping Wang. 2020. Modeling Intra and Inter-modality Incongruity for Multi-Modal Sarcasm Detection. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 1383–1392, Online. Association for Computational Linguistics.
- Cite (Informal):
- Modeling Intra and Inter-modality Incongruity for Multi-Modal Sarcasm Detection (Pan et al., Findings 2020)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2020.findings-emnlp.124.pdf