Towards Multi-Modal Sarcasm Detection via Hierarchical Congruity Modeling with Knowledge Enhancement
Abstract
Sarcasm is a linguistic phenomenon indicating a discrepancy between literal meanings and implied intentions. Due to its sophisticated nature, it is usually difficult to be detected from the text itself. As a result, multi-modal sarcasm detection has received more and more attention in both academia and industries. However, most existing techniques only modeled the atomic-level inconsistencies between the text input and its accompanying image, ignoring more complex compositions for both modalities. Moreover, they neglected the rich information contained in external knowledge, e.g., image captions. In this paper, we propose a novel hierarchical framework for sarcasm detection by exploring both the atomic-level congruity based on multi-head cross attentions and the composition-level congruity based on graph neural networks, where a post with low congruity can be identified as sarcasm. In addition, we exploit the effect of various knowledge resources for sarcasm detection. Evaluation results on a public multi-modal sarcasm detection dataset based on Twitter demonstrate the superiority of our proposed model.- Anthology ID:
- 2022.emnlp-main.333
- Volume:
- Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Editors:
- Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4995–5006
- Language:
- URL:
- https://aclanthology.org/2022.emnlp-main.333
- DOI:
- 10.18653/v1/2022.emnlp-main.333
- Cite (ACL):
- Hui Liu, Wenya Wang, and Haoliang Li. 2022. Towards Multi-Modal Sarcasm Detection via Hierarchical Congruity Modeling with Knowledge Enhancement. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 4995–5006, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- Towards Multi-Modal Sarcasm Detection via Hierarchical Congruity Modeling with Knowledge Enhancement (Liu et al., EMNLP 2022)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2022.emnlp-main.333.pdf