Gated Mechanism for Attention Based Multimodal Sentiment Analysis

ABSTRACT different granularities [3, 9] or use a cross interaction block that couple the features from different modalities [10, 6]. It is imperative that all modalities in multimodal interactions and 3. Fusion of unimodal and cross Therefore, to learn better cross modal information, we introduce 1.6% and 1.34% absolute improvement over current state-ofthe-art. Furthermore, to capture long term dependencies across 1. INTRODUCTION These are categorised into three types, 1. Methods that learn the modalities independently and fuse the In our proposed model, we aim to learn the interaction between [3, 4], and 3. Methods that explicitly learn contributions Personal use of this material is permitted. Multimodal sentiment analysis provides an opportunity to 2.1. M T V H T W H T V; W R d d (3) (U 1, U 2,..., U u) for a Text modality can be defined as: Cross attentive representations of Text (C V T R u d) and H T Bi-GRU(U 1, U 2,..., U u) (1) Video (C T V R u d) can be represented as: Subscript T denotes Text modality, A and V represent Audio As much as there is an opportunity to leverage cross modal interactions, representations is employed.