Bridging the Gap between Pre-Training and Fine-Tuning for End-to-End Speech Translation
Table 7: Ablation study for subword-level experiments.
['System', 'tst2010', 'tst2013', 'tst2014', 'tst2015']
In ‘-MT noise’ setting, we do not add noise to source sentences for MT. In ‘-weight sharing’ setting, we use different parameters in CTC classification layer and source embedding layer. These two experiments prove that both weight sharing and using noisy MT input benefit to the final translation quality. Performance degrades more in ‘-weight sharing’, indicating the semantic consistency contributes more to our model.