Empirical Investigation of Optimization Algorithms in Neural Machine Translation
2017 ◽
Vol 108
(1)
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pp. 13-25
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Keyword(s):
AbstractTraining neural networks is a non-convex and a high-dimensional optimization problem. In this paper, we provide a comparative study of the most popular stochastic optimization techniques used to train neural networks. We evaluate the methods in terms of convergence speed, translation quality, and training stability. In addition, we investigate combinations that seek to improve optimization in terms of these aspects. We train state-of-the-art attention-based models and apply them to perform neural machine translation. We demonstrate our results on two tasks: WMT 2016 En→Ro and WMT 2015 De→En.
2019 ◽
Vol 35
(2)
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pp. 147-166
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2019 ◽
Vol 33
◽
pp. 7338-7345
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2019 ◽
Vol 33
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pp. 6618-6625
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Keyword(s):
2018 ◽
Vol 110
(1)
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pp. 43-70
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2019 ◽
Vol 33
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pp. 6292-6299
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