  We show that the state of the art Transformer Machine Translation (MT) model
is not biased towards monotonic reordering (unlike previous recurrent neural
network models), but that nevertheless, long-distance dependencies remain a
challenge for the model. Since most dependencies are short-distance, common
evaluation metrics will be little influenced by how well systems perform on
them. We, therefore, propose an automatic approach for extracting challenge
sets replete with long-distance dependencies and argue that evaluation using
this methodology provides a complementary perspective on system performance. To
support our claim, we compile challenge sets for English-German and
German-English, which are much larger than any previously released challenge
set for MT. The extracted sets are large enough to allow reliable automatic
evaluation, which makes the proposed approach a scalable and practical solution
for evaluating MT performance on the long-tail of syntactic phenomena.
