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The combination of machines and humans for translation is effective, withmany studies showing productivity gains when humans post-editmachine-translated output instead of translating from scratch. To take fulladvantage of this combination, we need a fine-grained understanding of howhuman translators work, and which post-editing styles are more effective thanothers. In this paper, we release and analyze a new dataset with document-levelpost-editing action sequences, including edit operations from keystrokes, mouseactions, and waiting times. Our dataset comprises 66,268 full document sessionspost-edited by 332 humans, the largest of the kind released to date. We showthat action sequences are informative enough to identify post-editorsaccurately, compared to baselines that only look at the initial and final text.We build on this to learn and visualize continuous representations ofpost-editors, and we show that these representations improve the downstreamtask of predicting post-editing time.

Translator2Vec: Understanding and Representing Human Post-Editors