Pretraining and multitask learning are widely used to improve the speech to
text translation performance. In this study, we are interested in training a
speech to text translation model along with an auxiliary text to text
translation task. We conduct a detailed analysis to understand the impact of
the auxiliary task on the primary task within the multitask learning framework.
Our analysis confirms that multitask learning tends to generate similar decoder
representations from different modalities and preserve more information from
the pretrained text translation modules. We observe minimal negative transfer
effect between the two tasks and sharing more parameters is helpful to transfer
knowledge from the text task to the speech task. The analysis also reveals that
the modality representation difference at the top decoder layers is still not
negligible, and those layers are critical for the translation quality. Inspired
by these findings, we propose three methods to improve translation quality.
First, a parameter sharing and initialization strategy is proposed to enhance
information sharing between the tasks. Second, a novel attention-based
regularization is proposed for the encoders and pulls the representations from
different modalities closer. Third, an online knowledge distillation is
proposed to enhance the knowledge transfer from the text to the speech task.
Our experiments show that the proposed approach improves translation
performance by more than 2 BLEU over a strong baseline and achieves
state-of-the-art results on the \textsc{MuST-C} English-German, English-French
and English-Spanish language pairs.

本文研究了在多任务学习框架下，辅助文本翻译任务对语音到文本翻译任务的影响及参数共享等解决方案，提出三种方法以提高翻译质量，实验结果表明该方法在几种语种翻译任务中相对于基线都达到了最新水平。