  Adapter modules were recently introduced as an efficient alternative to
fine-tuning in NLP. Adapter tuning consists in freezing pretrained parameters
of a model and injecting lightweight modules between layers, resulting in the
addition of only a small number of task-specific trainable parameters. While
adapter tuning was investigated for multilingual neural machine translation,
this paper proposes a comprehensive analysis of adapters for multilingual
speech translation (ST). Starting from different pre-trained models (a
multilingual ST trained on parallel data or a multilingual BART (mBART) trained
on non-parallel multilingual data), we show that adapters can be used to: (a)
efficiently specialize ST to specific language pairs with a low extra cost in
terms of parameters, and (b) transfer from an automatic speech recognition
(ASR) task and an mBART pre-trained model to a multilingual ST task.
Experiments show that adapter tuning offer competitive results to full
fine-tuning, while being much more parameter-efficient.
