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With multilingual machine translation (MMT) models continuing to grow in sizeand number of supported languages, it is natural to reuse and upgrade existingmodels to save computation as data becomes available in more languages.However, adding new languages requires updating the vocabulary, whichcomplicates the reuse of embeddings. The question of how to reuse existingmodels while also making architectural changes to provide capacity for both oldand new languages has also not been closely studied. In this work, we introducethree techniques that help speed up effective learning of the new languages andalleviate catastrophic forgetting despite vocabulary and architecturemismatches. Our results show that by (1) carefully initializing the network,(2) applying learning rate scaling, and (3) performing data up-sampling, it ispossible to exceed the performance of a same-sized baseline model with 30%computation and recover the performance of a larger model trained from scratchwith over 50% reduction in computation. Furthermore, our analysis reveals thatthe introduced techniques help learn the new directions more effectively andalleviate catastrophic forgetting at the same time. We hope our work will guideresearch into more efficient approaches to growing languages for these MMTmodels and ultimately maximize the reuse of existing models.

Efficiently Upgrading Multilingual Machine Translation Models to Support More Languages