  Pre-Training (PT) of text representations has been successfully applied to
low-resource Neural Machine Translation (NMT). However, it usually fails to
achieve notable gains (sometimes, even worse) on resource-rich NMT on par with
its Random-Initialization (RI) counterpart. We take the first step to
investigate the complementarity between PT and RI in resource-rich scenarios
via two probing analyses, and find that: 1) PT improves NOT the accuracy, but
the generalization by achieving flatter loss landscapes than that of RI; 2) PT
improves NOT the confidence of lexical choice, but the negative diversity by
assigning smoother lexical probability distributions than that of RI. Based on
these insights, we propose to combine their complementarities with a model
fusion algorithm that utilizes optimal transport to align neurons between PT
and RI. Experiments on two resource-rich translation benchmarks, WMT'17
English-Chinese (20M) and WMT'19 English-German (36M), show that PT and RI
could be nicely complementary to each other, achieving substantial improvements
considering both translation accuracy, generalization, and negative diversity.
Probing tools and code are released at: https://github.com/zanchangtong/PTvsRI.
