Word embeddings are 300 dimensional, pretrained ourselves using word2vec (Mikolov et al., 2013)  To train our model, we compute gradient by back-propagation and apply stochastic gradient descent with mini-batch 200.
rNNs bury illuminating information under a complicated neural architecture.