Many language pairs are low resource - the amount and/or quality of  parallel data is not sufficient to train a neural machine translation (NMT) model which can reach an acceptable standard of accuracy. Many works have explored the use of the easier-to-get monolingual data to improve the performance of translation models in this category of languages - and even high  resource languages. The most successful of such works is the back-translation - using the translations of the target language monolingual data to increase the amount of the training data. The quality of the backward model - trained on the available parallel data - has been shown to determine the performance of the back-translation approach. Many approaches have been explored to improve the performance of thismodel especially in low resource languages where the amount of parallel data is not sufficient to train an acceptable backward model . Among such works are the use of self-learning and the iterative back-translation. These methods were shown to perform better than the standard back-translation. 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. Over several iterations, the synthetic datagenerated by the backward model is used to improve its performance through forward translation. Experiments have shown that the method outperforms both the standard back-translation and self-learning approach on IWSLT'14 English German low resource NMT. While the method also outperforms the iterative back-translation , though slightly, the number of models required to be trained is reduced exactly by the number of iterations .
Many language pairs are low resource , meaning the amount and/or quality of available parallel data is not sufficient to train a neural machine translation (NMT) model which can reach an acceptable standard of accuracy. Many works have explored using the readily available monolingual data in either or both of the languages to improve the standard of translation models in low, and even high , resource languages. One of the most successful of such works is the back-translation that utilizes the translations of the target language monolingual data to increase the amount of the training data. The quality of the backward model which is trained on the available parallel data has been shown to determine the performance of the back-translation approach. Despite this, only the forward model is improved on the monolingual target data in standard back-translation. 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 and back-translation respectively. Experimental results have shown the superiority of the proposed approach over the traditional back-translation method on English-German low resource neural machine translation. We also proposed an iterative self-learning approach that outperforms the iterative back-translation while also relying only on the monolingual target data and require the training of less models .
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