  Recent research in neural machine translation has explored flexible
generation orders, as an alternative to left-to-right generation. However,
training non-monotonic models brings a new complication: how to search for a
good ordering when there is a combinatorial explosion of orderings arriving at
the same final result? Also, how do these automatic orderings compare with the
actual behaviour of human translators? Current models rely on manually built
biases or are left to explore all possibilities on their own. In this paper, we
analyze the orderings produced by human post-editors and use them to train an
automatic post-editing system. We compare the resulting system with those
trained with left-to-right and random post-editing orderings. We observe that
humans tend to follow a nearly left-to-right order, but with interesting
deviations, such as preferring to start by correcting punctuation or verbs.

Computation and Language;Artificial Intelligence;Machine Learning