Recurrent Stacking of Layers for Compact Neural Machine Translation Models

Raj Dabre; Atsushi Fujita

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arXiv · 1807.05353

Recurrent Stacking of Layers for Compact Neural Machine Translation Models

Raj Dabre·Atsushi Fujita

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Abstract

In neural machine translation (NMT), the most common practice is to stack a number of recurrent or feed-forward layers in the encoder and the decoder. As a result, the addition of each new layer improves the translation quality significantly. However, this also leads to a significant increase in the number of parameters. In this paper, we propose to share parameters across all the layers thereby leading to a recurrently stacked NMT model. We empirically show that the translation quality of a model that recurrently stacks a single layer 6 times is comparable to the translation quality of a model that stacks 6 separate layers. We also show that using pseudo-parallel corpora by back-translation leads to further significant improvements in translation quality.

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BibTeX RIS

Raj Dabre, Atsushi Fujita. 2018-07-14. Recurrent Stacking of Layers for Compact Neural Machine Translation Models. https://arxiv.org/abs/1807.05353

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