Specifically, we investigate the use of three widely deployed representations: one-hot representations, distributed word representations learned from a neural network model, and distributed sentence representations computed with recursive autoencoder.
to leverage the different advantages and focuses, in terms of benefiting evaluation, of various representations, we concatenate the three representations to form one vector representation for each sentence.