I am training a VAE on a NLP task, I found that the reconstruction part is really memory consuming. Just wonder if there is any other method to reconstruct the input but without using auto-regressive.

I have thought about 1. Negative sampling, like what word2vec does, then we don’t need to normalize on the whole vocabulary 2. Bag of words, just simply averaging the word vectors and then use it as sentence vector, then reconstruct this sentence vector by minimizing MSE. Since this method reconstruct only one vector for one sentence instead every words, this will be fast and memory friendly. But I am not sure if this can work, or if the neural network will learn some trivial representation instead, e.g. all zeros

Any other idea? Thanks~