  To alleviate the data scarcity problem in End-to-end speech translation (ST),
pre-training on data for speech recognition and machine translation is
considered as an important technique. However, the modality gap between speech
and text prevents the ST model from efficiently inheriting knowledge from the
pre-trained models. In this work, we propose AdaTranS for end-to-end ST. It
adapts the speech features with a new shrinking mechanism to mitigate the
length mismatch between speech and text features by predicting word boundaries.
Experiments on the MUST-C dataset demonstrate that AdaTranS achieves better
performance than the other shrinking-based methods, with higher inference speed
and lower memory usage. Further experiments also show that AdaTranS can be
equipped with additional alignment losses to further improve performance.
