  Multilingual neural machine translation (MNMT) aims to translate multiple
languages with a single model and has been proved successful thanks to
effective knowledge transfer among different languages with shared parameters.
However, it is still an open question which parameters should be shared and
which ones need to be task-specific. Currently, the common practice is to
heuristically design or search language-specific modules, which is difficult to
find the optimal configuration. In this paper, we propose a novel parameter
differentiation based method that allows the model to determine which
parameters should be language-specific during training. Inspired by cellular
differentiation, each shared parameter in our method can dynamically
differentiate into more specialized types. We further define the
differentiation criterion as inter-task gradient similarity. Therefore,
parameters with conflicting inter-task gradients are more likely to be
language-specific. Extensive experiments on multilingual datasets have
demonstrated that our method significantly outperforms various strong baselines
with different parameter sharing configurations. Further analyses reveal that
the parameter sharing configuration obtained by our method correlates well with
the linguistic proximities.
