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arXiv · 2010.03412

Dual Reconstruction: a Unifying Objective for Semi-Supervised Neural Machine Translation

Weijia Xu·Xing Niu·Marine Carpuat
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Abstract

While Iterative Back-Translation and Dual Learning effectively incorporate monolingual training data in neural machine translation, they use different objectives and heuristic gradient approximation strategies, and have not been extensively compared. We introduce a novel dual reconstruction objective that provides a unified view of Iterative Back-Translation and Dual Learning. It motivates a theoretical analysis and controlled empirical study on German-English and Turkish-English tasks, which both suggest that Iterative Back-Translation is more effective than Dual Learning despite its relative simplicity.

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Weijia Xu, Xing Niu, Marine Carpuat. 2020-10-07. Dual Reconstruction: a Unifying Objective for Semi-Supervised Neural Machine Translation. https://arxiv.org/abs/2010.03412

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