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We propose a modular architecture of language-specific encoder-decoders thatconstitutes a multilingual machine translation system that can be incrementallyextended to new languages without the need for retraining the existing systemwhen adding new languages. Differently from previous works, we simultaneouslytrain $N$ languages in all translation directions by alternately freezingencoder or decoder modules, which indirectly forces the system to train in acommon intermediate representation for all languages. Experimental results frommultilingual machine translation show that we can successfully train thismodular architecture improving on the initial languages while falling slightlybehind when adding new languages or doing zero-shot translation. Additionalcomparison of the quality of sentence representation in the task of naturallanguage inference shows that the alternately freezing training is alsobeneficial in this direction.

Training Multilingual Machine Translation by Alternately Freezing Language-Specific Encoders-Decoders