  Previous studies show that intermediate supervision signals benefit various
Natural Language Processing tasks. However, it is not clear whether there exist
intermediate signals that benefit Neural Machine Translation (NMT). Borrowing
techniques from Statistical Machine Translation, we propose intermediate
signals which are intermediate sequences from the "source-like" structure to
the "target-like" structure. Such intermediate sequences introduce an inductive
bias that reflects a domain-agnostic principle of translation, which reduces
spurious correlations that are harmful to out-of-domain generalisation.
Furthermore, we introduce a full-permutation multi-task learning to alleviate
the spurious causal relations from intermediate sequences to the target, which
results from exposure bias. The Minimum Bayes Risk decoding algorithm is used
to pick the best candidate translation from all permutations to further improve
the performance. Experiments show that the introduced intermediate signals can
effectively improve the domain robustness of NMT and reduces the amount of
hallucinations on out-of-domain translation. Further analysis shows that our
methods are especially promising in low-resource scenarios.
