Multilingual Speech Translation with Efficient Finetuning of Pretrained Models
This work addresses the problem of high-quality speech translation with low training costs for multilingual applications, representing an incremental advance in transfer learning efficiency.
The paper tackles multilingual speech-to-text translation by efficiently finetuning pretrained models, achieving state-of-the-art results with an average BLEU improvement of +6.4 for English-to-other languages and +5.1 for other languages-to-English on the CoVoST 2 benchmark.
We present a simple yet effective approach to build multilingual speech-to-text (ST) translation by efficient transfer learning from pretrained speech encoder and text decoder. Our key finding is that a minimalistic LNA (LayerNorm and Attention) finetuning can achieve zero-shot crosslingual and cross-modality transfer ability by only finetuning less than 10% of the pretrained parameters. This enables effectively leveraging large pretrained models with low training cost. Using wav2vec 2.0 for acoustic modeling, and mBART for multilingual text generation, our approach advanced the new state-of-the-art for 34 translation directions (and surpassing cascaded ST for 23 of them) on large-scale multilingual ST benchmark CoVoST 2 (+6.4 BLEU on average across 15 En-X directions and +5.1 BLEU on average across 19 X-En directions). Our approach demonstrates strong zero-shot performance in a many-to-many multilingual model (+5.7 BLEU on average across 18 non-English directions), making it an appealing approach for attaining high-quality speech translation with improved parameter and data efficiency.