Hi friends!
I’m using an LSTM RNN to do character generation similar to the Shakespeare generator. My characters are encoded in a one-hot vector.
I want to experiment with word-level analysis. My question is: can I train a word2vec model on my corpus and use that to embed the corpus before feeding it into the LSTM RNN model?
I’ve tried using embedding on my own and, no surprise, the accuracy does not get any better. I’m assuming this is because there’s no inherent “meaning” in the embedding. If I can create “meaning” by training my own word2vec on the corpus, would that help the LSTM RNN train?
Thanks in advance for your time,
C