Representation and Granularity Joint Alignment Framework for Multimodal Sarcasm Detection on Social Media
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-5575-2_17
Reference21 articles.
1. Eke, C.I., Norman, A.A., Shuib, L., et al.: Sarcasm identification in textual data: systematic review, research challenges and open directions. Artif. Intell. Rev. 53, 4215–4258 (2020)
2. Xu, N., Zeng, Z., Mao, W.: Reasoning with multimodal sarcastic tweets via modeling cross-modality contrast and semantic association. In: Proceedings of ACL, pp. 3777–3786 (2020)
3. Tay, Y., Luu, A.T., Hui, S.C., Su, J.: Reasoning with sarcasm by reading in-between. In: Proceedings of ACL, pp. 1010–1020 (2018)
4. Cai, Y., Cai, H., Wan, X.: Multi-modal sarcasm detection in Twitter with hierarchical fusion model. In: Proceedings of ACL, Florence, Italy, pp. 2506–2515 (2019)
5. Pan, H., Lin, Z., Fu, P., Qi, Y., Wang, W.: Modeling intra and inter-modality incongruity for multi-modal sarcasm detection. In: Proceedings of EMNLP, pp. 1383–1392 (2020)
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