Word embeddings play a crucial role in our model, since they allow us to model complex relations between the translations and the reference using syntactic and semantic vector representations.
we further experimented with a setup where we first used the logistic cost to pretrain the neural network, and then we switched to the Kendall cost in order to perform some finer tuning.