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

Automatically Extracting Challenge Sets for Non local Phenomena in Neural Machine Translation