Worse WER, but Better BLEU? Leveraging Word Embedding as Intermediate in Multitask End-to-End Speech Translation
Table 2: Eigenvector similarity.
['[EMPTY]', '160 hours  [ITALIC] dev', '160 hours  [ITALIC] test', '40 hours  [ITALIC] dev', '40 hours  [ITALIC] test']
Eigenvector Similarity: To verify our proposed methods can constrain hidden states in the word embedding space, we computed eigenvector similarity between predicted embedding and pre-trained embedding space. The metric derives from Laplacian eigenvalues and represents how similar between two spaces, the lower value on the metric, the more approximately isospectral between the two spaces. Previous works showed that the metric is correlated to the performance of translation task Søgaard et al. CD is the most similar case among the three cases, and ME is the most different case. Results indicated that our proposals constrain hidden states in pre-trained embedding space.