  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.
