Before revision:  This work presents the iterative self-training approach as an improvement over the self-learning approach to further enhance the performance of the backward model.
After revision:  A previous study proposed an iterative back-translation approach for improving both models over several iterations. But unlike in the traditional back-translation, it relied on both the target and source monolingual data. This work, therefore, proposes a novel approach that enables both the backward and forward models to benefit from the monolingual target data through a hybrid of self-learning approach to further enhance the performance of the backward model.