Does it make sense to expand word embeddings so that each array index is a feature input or should the embedding itself be a model input? If you are building a DNN, say, with two layers, and you want to use embeddings as one of your feature inputs, what's the best way to input the embedding?

I'm trying to understand if I should break the embeddings up so that every array value in the embedding becomes its own input feature to the model or whether the embedding should be kept in array form.

I've been following AirBnB's model for inspiration.

I'm trying to predict a binary classification in the final layer.
Breaking the array you lose distributed representation-point of word embedding.

There is information in other dimensions (words) that are distributed in the word-embedding representation, breaking them up makes no sense.