Bridging the Gap between Pre-Training and Fine-Tuning for End-to-End Speech Translation
Table 4: BLEU comparison of cascaded results and our best end-to-end results. re-seg denotes the ASR outputs are re-segmented before fed into the MT model.
['[EMPTY]', 'tst2010', 'tst2013', 'tst2014', 'tst2015']
In addition to a simple combination system, we also re-segment the ASR outputs before feeding to the MT system, denoted as ‘cascaded+re-seg’. After that, we use the seq2seq model to add sentence boundaries and punctuation on ASR outputs. It can be seen that our end-to-end model outperforms the simple cascaded model over 2 BLEU scores, and achieves a comparable performance with the ‘cascaded+re-seg’ system.