Is sequence concatenation the best way to use multiple sequences in a GRU/LSTM model? I'm working on a model and I've got two string columns that I turn into sequences to then perform multi classification. As of now I'm concatenating both columns to create a longer sequence and use that as the model's initial input. This doesn't seem right to me, as this implies one string is the continuation of the other. So, my question is: is there a better way to use two sources of sequences into a model? Can two layers run in parallel and then be concatenated? What's the usual way to deal with this?
Thanks a lot
Clarification edit: my model takes sequential data (in this case a string column), then goes through an embedding layer and a couple of other layers to finally output a classification that is 1 out of the 36 possible classes. The thing is I've got two string columns with useful information and I'd like to use both in some way that makes sense.

I would compute dense feature vectors for the two strings separately, then concatenate, then use a FCN to make the classification decision. In psuedocode, it would look something like
representation1 = additional_layers(embed(string1))
representation2 = additional_layers(embed(string2))
combined = concatenate([representation1, representation2])
y_hat = fully_connected(combined)

The reasoning is that string1 isn't a continuation of string2, so it should be treated separately. But once we have a dense representation of the string, we can use an ordinary fully-connected network to classify it.
I don't know if the data in string1 is dramatically different from the data in string2. You could use distinct networks for each string, or re-use the same networks for both strings, depending on what you think is more appropriate.
