  The combination of machines and humans for translation is effective, with
many studies showing productivity gains when humans post-edit
machine-translated output instead of translating from scratch. To take full
advantage of this combination, we need a fine-grained understanding of how
human translators work, and which post-editing styles are more effective than
others. In this paper, we release and analyze a new dataset with document-level
post-editing action sequences, including edit operations from keystrokes, mouse
actions, and waiting times. Our dataset comprises 66,268 full document sessions
post-edited by 332 humans, the largest of the kind released to date. We show
that action sequences are informative enough to identify post-editors
accurately, compared to baselines that only look at the initial and final text.
We build on this to learn and visualize continuous representations of
post-editors, and we show that these representations improve the downstream
task of predicting post-editing time.
