  Multilingual neural machine translation with a single model has drawn much
attention due to its capability to deal with multiple languages. However, the
current multilingual translation paradigm often makes the model tend to
preserve the general knowledge, but ignore the language-specific knowledge.
Some previous works try to solve this problem by adding various kinds of
language-specific modules to the model, but they suffer from the parameter
explosion problem and require specialized manual design. To solve these
problems, we propose to divide the model neurons into general and
language-specific parts based on their importance across languages. The general
part is responsible for preserving the general knowledge and participating in
the translation of all the languages, while the language-specific part is
responsible for preserving the language-specific knowledge and participating in
the translation of some specific languages. Experimental results on several
language pairs, covering IWSLT and Europarl corpus datasets, demonstrate the
effectiveness and universality of the proposed method.
