  Previous neural machine translation models used some heuristic search
algorithms (e.g., beam search) in order to avoid solving the maximum a
posteriori problem over translation sentences at test time. In this paper, we
propose the Gumbel-Greedy Decoding which trains a generative network to predict
translation under a trained model. We solve such a problem using the
Gumbel-Softmax reparameterization, which makes our generative network
differentiable and trainable through standard stochastic gradient methods. We
empirically demonstrate that our proposed model is effective for generating
sequences of discrete words.
