Mutual-Learning Improves End-to-End Speech Translation

A currently popular research area in end-toend speech translation is the use of knowledge distillation from a machine translation (MT) task to improve the speech translation (ST) task. However, such scenario obviously allows only a one way transfer, limiting the overall effectiveness of the approach by the performance of the pre-trained teacher model. Therefore, we pose that in this respect knowledge distillationbased approaches are sub-optimal. We propose an alternative-a trainable mutual-learning scenario, where the MT and ST models are collaboratively trained and are considered as peers, rather than teacher/student. This allows us to improve the performance of end-to-end ST more effectively than with a teacher-student paradigm. As a side benefit, performance of the MT model also improves. Experimental results show that in our mutual-learning scenario, models can effectively utilise the auxiliary information from peer models and achieve compelling results on MuST-C datasets.