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Data selection has proven its merit for improving Neural Machine Translation(NMT), when applied to authentic data. But the benefit of using synthetic datain NMT training, produced by the popular back-translation technique, raises thequestion if data selection could also be useful for synthetic data?  In this work we use Infrequent N-gram Recovery (INR) and Feature DecayAlgorithms (FDA), two transductive data selection methods to obtain subsets ofsentences from synthetic data. These methods ensure that selected sentencesshare n-grams with the test set so the NMT model can be adapted to translateit.  Performing data selection on back-translated data creates new challenges asthe source-side may contain noise originated by the model used in theback-translation. Hence, finding n-grams present in the test set become moredifficult. Despite that, in our work we show that adapting a model with aselection of synthetic data is an useful approach.

Adaptation of Machine Translation Models with Back-translated Data using Transductive Data Selection Methods