Shaodi You


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JTAV: Jointly Learning Social Media Content Representation by Fusing Textual, Acoustic, and Visual Features
Hongru Liang | Haozheng Wang | Jun Wang | Shaodi You | Zhe Sun | Jin-Mao Wei | Zhenglu Yang
Proceedings of the 27th International Conference on Computational Linguistics

Learning social media content is the basis of many real-world applications, including information retrieval and recommendation systems, among others. In contrast with previous works that focus mainly on single modal or bi-modal learning, we propose to learn social media content by fusing jointly textual, acoustic, and visual information (JTAV). Effective strategies are proposed to extract fine-grained features of each modality, that is, attBiGRU and DCRNN. We also introduce cross-modal fusion and attentive pooling techniques to integrate multi-modal information comprehensively. Extensive experimental evaluation conducted on real-world datasets demonstrate our proposed model outperforms the state-of-the-art approaches by a large margin.