Source and Target Bidirectional Knowledge Distillation for End-to-end Speech Translation

Understand

A conventional approach to improving the performance of end-to-end speech translation (E2E-ST) models is to leverage the source transcription via pre-training and joint training with automatic speech recognition (ASR) and neural machine translation (NMT) tasks.

• However, since the input modalities are different, it is difficult to leverage source language text successfully.
• In this work, we focus on sequence-level knowledge distillation (SeqKD) from external text-based NMT models.
• To leverage the full potential of the source language information, we propose backward SeqKD, SeqKD from a target-to-source backward NMT model.