I am doing sentiment classification (reviews classification) using an LSTM model and I want to enhance performance by allowing the model to consume static features, such as age and gender.
My aim is to introduce this static auxiliary features outside of the LSTM by means of additional fully connected layers. You might have a data flow like this:
SEQUENCE_INPUTS ------> LSTM --------> |---> MERGE ---> SIGMOID STATIC_INPUTS -----> Preprocessing -->
I am struggling with how to efficiently batch the data for this task e.g. should batching take place separately for sequential and static features or package all feature types together? It would be very helpful to see similar implementations from the community.