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Differently from the traditional statistical MT that decomposes thetranslation task into distinct separately learned components, neural machinetranslation uses a single neural network to model the entire translationprocess. Despite neural machine translation being de-facto standard, it isstill not clear how NMT models acquire different competences over the course oftraining, and how this mirrors the different models in traditional SMT. In thiswork, we look at the competences related to three core SMT components and findthat during training, NMT first focuses on learning target-side languagemodeling, then improves translation quality approaching word-by-wordtranslation, and finally learns more complicated reordering patterns. We showthat this behavior holds for several models and language pairs. Additionally,we explain how such an understanding of the training process can be useful inpractice and, as an example, show how it can be used to improve vanillanon-autoregressive neural machine translation by guiding teacher modelselection.

Language Modeling, Lexical Translation, Reordering: The Training Process of NMT through the Lens of Classical SMT