  Significant improvements in end-to-end speech translation (ST) have been
achieved through the application of multi-task learning. However, the extent to
which auxiliary tasks are highly consistent with the ST task, and how much this
approach truly helps, have not been thoroughly studied. In this paper, we
investigate the consistency between different tasks, considering different
times and modules. We find that the textual encoder primarily facilitates
cross-modal conversion, but the presence of noise in speech impedes the
consistency between text and speech representations. Furthermore, we propose an
improved multi-task learning (IMTL) approach for the ST task, which bridges the
modal gap by mitigating the difference in length and representation. We conduct
experiments on the MuST-C dataset. The results demonstrate that our method
attains state-of-the-art results. Moreover, when additional data is used, we
achieve the new SOTA result on MuST-C English to Spanish task with 20.8% of the
training time required by the current SOTA method.
