GATED MECHANISM FOR ATTENTION BASED MULTIMODAL SENTIMENT ANALYSIS
Table 1: Comparison of performance of each step in the proposed model. Accuracy values are mentioned in the table
['[BOLD] Sl. No.', '[BOLD] Model', '[BOLD] CMU-MOSI', '[BOLD] CMU-MOSEI']
We frame a unimodal (B1) and a bimodal baseline (B3) to compare the impacts of self attention (B2) and gating mechanism (B4). Further we also evaluate the model with deep multimodal fusion (B6). We see that by using self attention, the performance of model improves by 0.54% on MOSI and MOSEI corpora. Gating mechanism improves the accuracy by absolute 1% on MOSI while multimodal fusion adds additional 0.54% and 0.26% accuracy on two corpora.