Knowledge-Preserving Pluggable Modules for Multilingual Speech Translation Tasks

Multilingual speech translation tasks typically employ retraining, regularization, or resampling methods to add new languages. Retraining the model significantly increases training time and cost. Moreover, using existing regularization or resampling methods to balance performance between new and original languages might lead to catastrophic forgetting. This can degrade the translation performance of the existing languages. To mitigate the above issues, we store the knowledge of new languages in additional models. We then introduce them as pluggable modules into existing multilingual speech translation models. This approach does not significantly increase training costs and affect the translation performance of existing models. The experimental results demonstrate that our method improves the translation performance of new languages without affecting existing translation tasks. Our code is available at https://github.com/myaxxxxx/transfer-st.