  We present in this paper a novel scheme for multimodal learning named the
Parallel Attention mechanism. In addition, to take into account the advantages
of grammar and context in Vietnamese, we propose the Hierarchical Linguistic
Features Extractor instead of using an LSTM network to extract linguistic
features. Based on these two novel modules, we introduce the Parallel Attention
Transformer (PAT), achieving the best accuracy compared to all baselines on the
benchmark ViVQA dataset and other SOTA methods including SAAA and MCAN.
