  Recently many efforts have been devoted to interpreting the black-box NMT
models, but little progress has been made on metrics to evaluate explanation
methods. Word Alignment Error Rate can be used as such a metric that matches
human understanding, however, it can not measure explanation methods on those
target words that are not aligned to any source word. This paper thereby makes
an initial attempt to evaluate explanation methods from an alternative
viewpoint. To this end, it proposes a principled metric based on fidelity in
regard to the predictive behavior of the NMT model. As the exact computation
for this metric is intractable, we employ an efficient approach as its
approximation. On six standard translation tasks, we quantitatively evaluate
several explanation methods in terms of the proposed metric and we reveal some
valuable findings for these explanation methods in our experiments.
