Worse WER, but Better BLEU? Leveraging Word Embedding as Intermediate in Multitask End-to-End Speech Translation
Table 3: Precision@k of semantic alignment on test set.
['[EMPTY]', '160 hours P@1', '160 hours P@5', '40 hours P@1', '40 hours P@5']
Semantic Alignment: To further verify if predicted embedding is semantically aligned to pre-trained embedding, we applied Procrustes alignment Conneau et al. Top 50 frequent words were selected to be the training dictionary, and we evaluated on the remaining 450 words with cross-domain similarity local scaling (CSLS) method. Precision@k (P@k, k=1,5) were reported as measurements. This experiment reinforced that our proposals can constrain hidden states to the similar structure of word embedding space.