A multitask co-training framework for improving speech translation by leveraging speech recognition and machine translation tasks-Reference-Cited by-同舟云学术

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A multitask co-training framework for improving speech translation by leveraging speech recognition and machine translation tasks

Published:2024-02-27 Issue:15 Volume:36 Page:8641-8656
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Zhou Yue,Yuan Yuxuan,Shi Xiaodong^ORCID

Funder

National key R &D Program of China

Major Scientific Research Project of the State Language Commission in the 13th Five-Year Plan

Key Support Project of NSFC-Liaoning Joint Foundation

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s00521-024-09547-8.pdf

Reference69 articles.

1. Fang Q, Ye R, Li L, Feng Y, Wang M (2022) Stemm: self-learning with speech-text manifold mixup for speech translation. In: Proc ACL, pp 7050–7062

2. Dong Q, Ye R, Wang M, Zhou H, Xu S, Xu B, Li L (2021) Listen, understand and translate: Triple supervision decouples end-to-end speech-to-text translation. In: Proc AAAI

3. Zhang P, Ge N, Chen B, Fan K (2019) Lattice transformer for speech translation. In: Proc ACL, pp 6475–6484

4. Lam TK, Schamoni S, Riezler S (2021) Cascaded models with cyclic feedback for direct speech translation. In: Proc ICASSP, pp 7508–7512 . IEEE

5. Dong Q, Wang F, Yang Z, Chen W, Xu S, Xu B (2019) Adapting translation models for transcript disfluency detection. In: Proc AAAI, vol 33, pp 6351–6358

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