Before revision:  Then, a novel graph  operation, called Multimodal Temporal Graph Attention , along with a dynamic pruning and read-out technique  is designed to efficiently process this multimodal temporal graph . By learning to focus only on the important interactions within the graph, our MTGAT is able to achieve state-of-the-art performance on multimodal sentiment analysis and emotion recognition benchmarks including IEMOCAP and CMU-MOSI , while utilizing significantly fewer computations .
After revision:  Then, a novel graph operation, called Multimodal Temporal Graph Attention , along with a dynamic pruning and read-out technique , is designed to efficiently process this multimodal temporal graph . By learning to focus only on the important interactions within the graph, our MTGAT is able to achieve state-of-the-art performance on multimodal sentiment analysis and emotion recognition benchmarks including IEMOCAP and CMU-MOSI , while utilizing significantly fewer computations .