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Multimodal Language Analysis with Recurrent Multistage Fusion
2018
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Computational modeling of human multimodal language is an emerging research area in natural language processing spanning the language, visual and acoustic modalities. Comprehending multimodal language requires modeling not only the interactions within each modality (intra-modal interactions) but more importantly the interactions between modalities (cross-modal interactions). In this paper, we propose the Recurrent Multistage Fusion Network (RMFN) which decomposes the fusion problem into
doi:10.18653/v1/d18-1014
dblp:conf/emnlp/LiangLZM18
fatcat:itt5akqzpjg4bluwwm42egd4h4