  Although teacher forcing has become the main training paradigm for neural
machine translation, it usually makes predictions only conditioned on past
information, and hence lacks global planning for the future. To address this
problem, we introduce another decoder, called seer decoder, into the
encoder-decoder framework during training, which involves future information in
target predictions. Meanwhile, we force the conventional decoder to simulate
the behaviors of the seer decoder via knowledge distillation. In this way, at
test the conventional decoder can perform like the seer decoder without the
attendance of it. Experiment results on the Chinese-English, English-German and
English-Romanian translation tasks show our method can outperform competitive
baselines significantly and achieves greater improvements on the bigger data
sets. Besides, the experiments also prove knowledge distillation the best way
to transfer knowledge from the seer decoder to the conventional decoder
compared to adversarial learning and L2 regularization.
