CKDST: Comprehensively and Effectively Distill Knowledge from Machine Translation to End-to-End Speech Translation

Yikun Lei, Zhengshan Xue, Xiaohu Zhao, Haoran Sun, ShaoLin Zhu, Xiaodong Lin, Deyi Xiong. CKDST: Comprehensively and Effectively Distill Knowledge from Machine Translation to End-to-End Speech Translation. In Anna Rogers, Jordan L. Boyd-Graber, Naoaki Okazaki, editors, Findings of the Association for Computational Linguistics: ACL 2023, Toronto, Canada, July 9-14, 2023. pages 3123-3137, Association for Computational Linguistics, 2023. [doi]

@inproceedings{LeiXZSZLX23,
  title = {CKDST: Comprehensively and Effectively Distill Knowledge from Machine Translation to End-to-End Speech Translation},
  author = {Yikun Lei and Zhengshan Xue and Xiaohu Zhao and Haoran Sun and ShaoLin Zhu and Xiaodong Lin and Deyi Xiong},
  year = {2023},
  url = {https://aclanthology.org/2023.findings-acl.195},
  researchr = {https://researchr.org/publication/LeiXZSZLX23},
  cites = {0},
  citedby = {0},
  pages = {3123-3137},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2023, Toronto, Canada, July 9-14, 2023},
  editor = {Anna Rogers and Jordan L. Boyd-Graber and Naoaki Okazaki},
  publisher = {Association for Computational Linguistics},
  isbn = {978-1-959429-62-3},
}