Neural Machine Translation with Gumbel-Greedy Decoding

Authors

  • Jiatao Gu The University of Hong Kong
  • Daniel Jiwoong Im AIFounded Inc.
  • Victor O.K. Li The University of Hong Kong

Keywords:

Machine Translation, Gumbel Softmax, Greedy Decoding, Generator Discriminator

Abstract

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 phase. In this paper, we propose the \textit{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.

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Published

2018-04-27

How to Cite

Gu, J., Im, D. J., & Li, V. O. (2018). Neural Machine Translation with Gumbel-Greedy Decoding. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/12016