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arXiv · 1706.07518

Neural Machine Translation with Gumbel-Greedy Decoding

Jiatao Gu·Daniel Jiwoong Im·Victor O. K. Li
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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 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.

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Jiatao Gu, Daniel Jiwoong Im, Victor O. K. Li. 2017-06-22. Neural Machine Translation with Gumbel-Greedy Decoding. https://arxiv.org/abs/1706.07518

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