  Evaluating translation models is a trade-off between effort and detail. On
the one end of the spectrum there are automatic count-based methods such as
BLEU, on the other end linguistic evaluations by humans, which arguably are
more informative but also require a disproportionately high effort. To narrow
the spectrum, we propose a general approach on how to automatically expose
systematic differences between human and machine translations to human experts.
Inspired by adversarial settings, we train a neural text classifier to
distinguish human from machine translations. A classifier that performs and
generalizes well after training should recognize systematic differences between
the two classes, which we uncover with neural explainability methods. Our
proof-of-concept implementation, DiaMaT, is open source. Applied to a dataset
translated by a state-of-the-art neural Transformer model, DiaMaT achieves a
classification accuracy of 75% and exposes meaningful differences between
humans and the Transformer, amidst the current discussion about human parity.
