Multimodal Transformer for Unaligned Multimodal Language Sequences
Table 5: Hyperparameters of Multimodal Transformer (MulT) we use for the various tasks. The “# of Crossmodal Blocks” and “# of Crossmodal Attention Heads” are for each transformer.
['[EMPTY]', 'CMU-MOSEI', 'CMU-MOSI', 'IEMOCAP']
As previously mentioned, the models are contained at roughly the same sizes as in prior works for the purpose of fair comparison. For hyperparameters such as the dropout rate and number of heads in crossmodal attention module, we perform a basic grid search. We decay the learning rate by a factor of 10 when the validation performance plateaus.