Worse WER, but Better BLEU? Leveraging Word Embedding as Intermediate in Multitask End-to-End Speech Translation (2020.acl-main)
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| Challenge: | Existing studies show that multitask learning improves speech translation performance by utilizing word embedding as the intermediate. |
| Approach: | They propose to use word embedding as an intermediate to improve multitask ST models by utilizing word embeds as input. |
| Outcome: | The proposed model outperforms existing models with sufficient training data but is still lacking in the low-resource scenario. |
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