  Multilingual neural machine translation can translate unseen language pairs
during training, i.e. zero-shot translation. However, the zero-shot translation
is always unstable. Although prior works attributed the instability to the
domination of central language, e.g. English, we supplement this viewpoint with
the strict dependence of non-centered languages. In this work, we propose a
simple, lightweight yet effective language-specific modeling method by adapting
to non-centered languages and combining the shared information and the
language-specific information to counteract the instability of zero-shot
translation. Experiments with Transformer on IWSLT17, Europarl, TED talks, and
OPUS-100 datasets show that our method not only performs better than strong
baselines in centered data conditions but also can easily fit non-centered data
conditions. By further investigating the layer attribution, we show that our
proposed method can disentangle the coupled representation in the correct
direction.
