  We report the results of our classification-based machine translation model,
built upon the framework of a recurrent neural network using gated recurrent
units. Unlike other RNN models that attempt to maximize the overall conditional
log probability of sentences against sentences, our model focuses a
classification approach of estimating the conditional probability of the next
word given the input sequence. This simpler approach using GRUs was hoped to be
comparable with more complicated RNN models, but achievements in this
implementation were modest and there remains a lot of room for improving this
classification approach.
