2020
ACL
ACL 2020
Worse WER, but Better BLEU? Leveraging Word Embedding as Intermediate in Multitask End-to-End Speech Translation
Abstract
AbstractSpeech translation (ST) aims to learn transformations from speech in the source language to the text in the target language. Previous works show that multitask learning improves the ST performance, in which the recognition decoder generates the text of the source language, and the translation decoder obtains the final translations based on the output of the recognition decoder. Because whether the output of the recognition decoder has the correct semantics is more critical than its accuracy, we propose to improve the multitask ST model by utilizing word embedding as the intermediate.
❓
The Questioner
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Interdisciplinary Bridge
— Artificial Intelligence and Deep Learning and Machine Learning and Natural Language Processing and Speech & Audio
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Keyword Pioneer
— recognition decoder
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Artificial Intelligence > Core AI > Multimodal Learning
Machine Learning > Core Methods > Classification
Speech & Audio > Recognition > Speech Recognition
Natural Language Processing > Generation > Machine Translation
Deep Learning > Learning Types > Multi-Task Learning
Artificial Intelligence > Core AI > Speech Processing
Speech & Audio > Recognition > Speech Translation