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While Iterative Back-Translation and Dual Learning effectively incorporatemonolingual training data in neural machine translation, they use differentobjectives and heuristic gradient approximation strategies, and have not beenextensively compared. We introduce a novel dual reconstruction objective thatprovides a unified view of Iterative Back-Translation and Dual Learning. Itmotivates a theoretical analysis and controlled empirical study onGerman-English and Turkish-English tasks, which both suggest that IterativeBack-Translation is more effective than Dual Learning despite its relativesimplicity.

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