Low Rank Fusion based Transformers for Multimodal Sequences

• URL: http://arxiv.org/abs/2007.02038v1
• Date: Sat, 4 Jul 2020 08:05:40 GMT
• Title: Low Rank Fusion based Transformers for Multimodal Sequences
• Authors: Saurav Sahay, Eda Okur, Shachi H Kumar, Lama Nachman
• Abstract summary: We present two methods for the Multimodal Sentiment and Emotion Recognition results on CMU-MOSEI, CMU-MOSI, and IEMOCAP datasets. We show that our models have lesser parameters, train faster and perform comparably to many larger fusion-based architectures.
• Score: 9.507869508188266
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Our senses individually work in a coordinated fashion to express our emotional intentions. In this work, we experiment with modeling modality-specific sensory signals to attend to our latent multimodal emotional intentions and vice versa expressed via low-rank multimodal fusion and multimodal transformers. The low-rank factorization of multimodal fusion amongst the modalities helps represent approximate multiplicative latent signal interactions. Motivated by the work of~\cite{tsai2019MULT} and~\cite{Liu_2018}, we present our transformer-based cross-fusion architecture without any over-parameterization of the model. The low-rank fusion helps represent the latent signal interactions while the modality-specific attention helps focus on relevant parts of the signal. We present two methods for the Multimodal Sentiment and Emotion Recognition results on CMU-MOSEI, CMU-MOSI, and IEMOCAP datasets and show that our models have lesser parameters, train faster and perform comparably to many larger fusion-based architectures.

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