  As part of a larger project on optimal learning conditions in neural machine
translation, we investigate characteristic training phases of translation
engines. All our experiments are carried out using OpenNMT-Py: the
pre-processing step is implemented using the Europarl training corpus and the
INTERSECT corpus is used for validation. Longitudinal analyses of training
phases suggest that the progression of translations is not always linear.
Following the results of textometric explorations, we identify the importance
of the phenomena related to chronological progression, in order to map
different processes at work in neural machine translation (NMT).
