What are the benefits and tradeoffs of a 1D conv vs a multi-input seq2seq LSTM model? I have 6 sequences, s1,..,s6. Using all sequences I want to predict a binary vector q = [0,0,0,1,1,1,0,0,0,1,1,1,...], which is a mask of the activity of the 6 sequences.

I have looked at seq2seq lstm models, but am struggling with the multiple-sequence-input and single-sequence-output architecture. Am I headed down the right path, or should I shift my focus to a convnet with 6 non-spatial dimensions, and 1 spatial dimension?

Thanks,
I suggest you transform your 6 sequences into 1 since they have the same lengths. You will have 1 sequence with more features for each timestamp.

It is hard to tell you which method would be better. But a LSTM model seems to be well suited for you.