  We introduce and study the problem of Continual Multilingual Learning (CML)
where a previously trained multilingual model is periodically updated using new
data arriving in stages. If the new data is present only in a subset of
languages, we find that the resulting model shows improved performance only on
the languages included in the latest update (and a few closely related
languages) while its performance on all the remaining languages degrade
significantly. We address this challenge by proposing LAFT-URIEL, a
parameter-efficient finetuning strategy which aims to increase the number of
languages on which the model improves after an update, while reducing the
magnitude of loss in performance for the remaining languages. LAFT-URIEL uses
linguistic knowledge to balance overfitting and knowledge sharing across
languages, allowing for an additional 25% of task languages to see an
improvement in performance after an update, while also reducing the average
magnitude of losses on the remaining languages by 78% relative.
