  Machine translation (MT) systems translate text between different languages
by automatically learning in-depth knowledge of bilingual lexicons, grammar and
semantics from the training examples. Although neural machine translation (NMT)
has led the field of MT, we have a poor understanding on how and why it works.
In this paper, we bridge the gap by assessing the bilingual knowledge learned
by NMT models with phrase table -- an interpretable table of bilingual
lexicons. We extract the phrase table from the training examples that an NMT
model correctly predicts. Extensive experiments on widely-used datasets show
that the phrase table is reasonable and consistent against language pairs and
random seeds. Equipped with the interpretable phrase table, we find that NMT
models learn patterns from simple to complex and distill essential bilingual
knowledge from the training examples. We also revisit some advances that
potentially affect the learning of bilingual knowledge (e.g.,
back-translation), and report some interesting findings. We believe this work
opens a new angle to interpret NMT with statistic models, and provides
empirical supports for recent advances in improving NMT models.
