For an LSTM model for sentence classification, I have text sequences for input. Using word embeddings, let’s say each token is mapped to 100D embeddings. Now if I also want to use other features, like part-of-speech, do I simply concatenate them and have 101D inputs? Doesn’t this diminish the effect of the POS tags?
Also, the embeddings are trainable and can be learned during training, while POS tags shouldn’t change. How is this possible if they’re just concatenated into one embedding layer?
I can’t seem to find a clear answer or an example describing the standard way to do this. It seems like some people concatenate, while others use two separate LSTMs then somehow combine those outputs?