Using ULMFit for text classification with multiple text fields

Hello All,

Working on genre classification for the movielens dataset. Apart from the movie description, the dataset consists of other features as well, like, cast, tagline, etc. I am wanting to use ULMFit to perform the text classification. What I am wanting to do is to add an embedding layer for each of these features. But being new to all this, I am a little unsure how to go forward. The AWD LSTM used in fastai has the encoder like this:
self.encoder = nn.Embedding(vocab_sz, emb_sz, padding_idx=pad_token)

Now, for each feature, the embedding layer will have a different vocab_sz . Do I need to concatenate them all and sum up the different vocab sizes to form a new embedding layer? Is that meaningful? If not, what is the best way to incorporate such features?