Neural Machine Translation with Gumbel-Greedy Decoding

06/22/2017
by   Jiatao Gu, et al.
0

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.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset